Offline Handwritten Signature Verification (offline HSV) analyzes static images of signatures to distinguish between genuine and forged samples. To create training data for such systems, negative samples, commonly known as random forgeries, are typically drawn from genuine signatures of other users, as real-world datasets often lack actual forgeries. While this strategy helps address the scarcity of forgery data, it faces several challenges. The randomly selected samples may lack the diversity and challenge needed to improve model robustness. Additionally, they can cause redundancy, increasing training time and storage requirements, and may introduce bias across users, leading to unfair training distributions. This paper proposes a novel strategy, called \textit{ProtoSig}, for generating more informative and diverse negative samples by leveraging prototypical signatures, which are compact, non-identifiable vectors obtained through a data-driven summarization of signature feature vectors. Our experiments demonstrate that ProtoSig enhances skilled forgery detection in a writer-dependent verification approach, eliminating performance variability across runs, while reducing dependence on external user data. We further demonstrate that our method achieves similar or better accuracy using a smaller dataset, offering considerable gains in scalability, computational efficiency, and storage requirements, while strengthening data privacy and promoting fairness in offline HSV systems.​

The divide-and-conquer strategy is a common approach for solving Computer Science problems, where one divides a problem into multiple potentially simpler subproblems whose results are combined. This decomposition approach can also help solve classification problems in Machine Learning (ML). In this paper, we propose a decomposition strategy to generate multiple views from a dataset in an optimized way. The objective is to divide the input features into subsets and build ML models for each. When a new instance has to be classified, the view for which the complexity in classifying the instance is lower is chosen to be used in prediction. We show experimentally how this approach can benefit the nearest neighbor classifier, increasing classification accuracy for complex problems while overcoming the limitations of this method in handling directly high-dimensional data.

Handwriting, as a uniquely human skill, contributes to fine motor development and cognitive growth. Beyond mere functionality, handwriting carries individuality and subtle emotional nuances, evoking feelings of intimacy and authenticity. Consequently, the generation of synthetic handwritten manuscripts should not only prioritize the production of legible text, but also seek to enhance personalization and authenticity in digital communication. This enhancement renders handwriting synthesis invaluable in domains such as digital marketing and e-learning. Notably, handwriting synthesis plays a pivotal role in forensic science, particularly in signature verification, to bolster security and prevent fraud. Additionally, it has the potential to enhance accessibility, particularly for individuals with disabilities, and assist in health monitoring among elderly populations. Motivated by the significance of handwriting synthesis, this paper conducts a comprehensive literature review on the synthetic generation of handwriting and signatures. By examining research from 2019 to 2024, we categorize methods of synthesis, evaluate synthetic handwriting quality, and explore practical applications. Furthermore, we provide insights into publicly available code resources and emerging synthetic databases.

Most dynamic ensemble selection (DES) methods utilize the K-Nearest Neighbors (KNN) algorithm to estimate the competence of classifiers in a small region surrounding the query sample. However, KNN is very sensitive to the local distribution of the data. Moreover, it also has a high computational cost as it requires storing the whole data in memory and performing multiple distance calculations during inference. Hence, the de-pendency on the KNN algorithm ends up limiting the use of DES techniques for large-scale problems. This paper presents a new DES framework based on fuzzy hyperboxes called FH-DES. Each hyperbox can represent a group of samples using only two data points (Min and Max corners). Thus, the hyperbox-based system will have less computational complexity than other dynamic se-lection methods. In addition, despite the KNN-based approaches, the fuzzy hyperbox is not sensitive to the local data distribution. Therefore, the local distribution of the samples does not affect the system's performance. Furthermore, in this research, for the first time, misclassified samples are used to estimate the competence of the classifiers, which has not been observed in previous fusion approaches. Experimental results demonstrate that the proposed method has high classification accuracy while having a lower complexity when compared with the state-of-the-art dynamic selection methods. The implemented code is available at https://github.com/redavtalab/FH-DES_IJCNN.git.

Class imbalance is a characteristic known for making learning more challenging for classification models as they may end up biased towards the majority class. A promising approach among the ensemble-based methods in the context of imbalance learning is Dynamic Selection (DS). DS techniques single out a subset of the classifiers in the ensemble to label each given unknown sample according to their estimated competence in the area surrounding the query. Because only a small region is taken into account in the selection scheme, the global class disproportion may have less impact over the system's performance. However, the presence of local class overlap may severely hinder the DS techniques' performance over imbalanced distributions as it not only exacerbates the effects of the under-representation but also introduces ambiguous and possibly unreliable samples to the competence estimation process. Thus, in this work, we propose a DS technique which attempts to minimize the effects of the local class overlap during the classifier selection procedure. The proposed method iteratively removes from the target region the instance perceived as the hardest to classify until a classifier is deemed competent to label the query sample. The known samples are characterized using instance hardness measures that quantify the local class overlap. Experimental results show that the proposed technique can significantly outperform the baseline as well as several other DS techniques, suggesting its suitability for dealing with class under-representation and overlap. Furthermore, the proposed technique still yielded competitive results when using an under-sampled, less overlapped version of the labelled sets, specially over the problems with a high proportion of minority class samples in overlap areas. Code available at https://github.com/marianaasouza/lords.

In writer-independent verification systems, a single model is trained for all users of the system using dissimilarity vectors obtained through a dichotomy transformation that converts a multi-class problem into a 2-class problem comprising: (i) the intra-class dissimilarity vectors computed from samples of the same user, (ii) the inter-class dissimilarity vectors computed from samples of different users. When mapping handwritten signature representations, it is desired to obtain well-separated dense clusters of signature representations for each user, in such a way that transformed intra-class dissimilarity vectors tend to be separated from the inter-class dissimilarity vectors. Moreover, since skilled forgeries resemble reference signatures, it is also desired to obtain skilled forgery dissimilarity vectors that are further away from the region of the intra-class dissimilarity vectors. In this work, it is hypothesized that an improved dissimilarity space can be achieved through a multi-task framework for learning handwritten signature feature representations based on deep contrastive learning. The proposed framework is composed of two objective-specific tasks; it does not use skilled forgeries for training. The first task aims to map signature examples of a given user in a dense cluster, while linearly separating the signature representations of different users. The second task aims to adjust forgery representations by adopting a contrastive loss with the ability to perform hard negative mining. Hard negatives are similar examples but from different classes that can be seen as artificially generated skilled forgeries for training. In a writer-independent verification approach, the model obtained with the proposed framework is evaluated in terms of the equal error rate on GPDS-300, CEDAR and MCYT-75 datasets. Experiments demonstrated a statistically significant improvement in signature verification compared to the state-of-the-art SigNet feature extraction method.

Usually, in a real-world scenario, few signature samples are available to train an automatic signature verification system (ASVS). However, such systems do indeed need a lot of signatures to achieve an acceptable performance. Neuromotor signature duplication methods and feature space augmentation methods may be used to meet the need for an increase in the number of samples. Such techniques manually or empirically define a set of parameters to introduce a degree of writer variability. Therefore, in the present study, a method to automatically model the most common writer variability traits is proposed. The method is used to generate offline signatures in the image and the feature space and train an ASVS. We also introduce an alternative approach to evaluate the quality of samples considering their feature vectors. We evaluated the performance of an ASVS with the generated samples using three well-known offline signature datasets: GPDS, MCYT-75, and CEDAR. In GPDS-300, when the SVM classifier was trained using one genuine signature per writer and the duplicates generated in the image space, the Equal Error Rate (EER) decreased from 5.71% to 1.08%. Under the same conditions, the EER decreased to 1.04% using the feature space augmentation technique. We also verified that the model that generates duplicates in the image space reproduces the most common writer variability traits in the three different datasets.

Multi-view learning is a learning task in which data is described by several concurrent representations. Its main challenge is most often to exploit the complementarities between these representations to help solve a classification/regression task. This is a challenge that can be met nowadays if there is a large amount of data available for learning. However, this is not necessarily true for all real-world problems, where data are sometimes scarce (e.g. problems related to the medical environment). In these situations, an effective strategy is to use intermediate representations based on the dissimilarities between instances. This work presents new ways of constructing these dissimilarity representations, learning them from data with Random Forest classifiers. More precisely, two methods are proposed, which modify the Random Forest proximity measure, to adapt it to the context of High Dimension Low Sample Size (HDLSS) multi-view classification problems. The second method, based on an Instance Hardness measurement, is significantly more accurate than other state-of-the-art measurements including the original RF Proximity measurement and the Large Margin Nearest Neighbor (LMNN) metric learning measurement.

This paper describes a classifier pool generation method guided by the diversity estimated on the data complexity and classifier decisions. First, the behavior of complexity measures is assessed by considering several subsamples of the dataset. The complexity measures with high variability across the subsamples are selected for posterior pool adaptation, where an evolutionary algorithm optimizes diversity in both complexity and decision spaces. A robust experimental protocol with 28 datasets and 20 replications is used to evaluate the proposed method. Results show significant accuracy improvements in 69.4% of the experiments when Dynamic Classifier Selection and Dynamic Ensemble Selection methods are applied.

SigNet is a state of the art model for feature representation used for handwritten signature verification (HSV). This representation is based on a Deep Convolutional Neural Network (DCNN) and contains 2048 dimensions. When transposed to a dissimilarity space generated by the dichotomy transformation (DT), related to the writer-independent (WI) approach, these features may include redundant information. This paper investigates the presence of overfitting when using Binary Particle Swarm Optimization (BPSO) to perform the feature selection in a wrapper mode. We proposed a method based on a global validation strategy with an external archive to control overfitting during the search for the most discriminant representation. Moreover, an investigation is also carried out to evaluate the use of the selected features in a transfer learning context. The analysis is carried out on a writer-independent approach on the CEDAR, MCYT and GPDS datasets. The experimental results showed the presence of overfitting when no validation is used during the optimization process and the improvement when the global validation strategy with an external archive is used. Also, the space generated after feature selection can be used in a transfer learning context.

An end-to-end solution for handwritten numeral string recognition is proposed, in which the numeral string is considered as composed of objects automatically detected and recognized by a YoLo-based model. The main contribution of this paper is to avoid heuristic-based methods for string preprocessing and segmentation, the need for task-oriented classifiers, and also the use of specific constraints related to the string length. A robust experimental protocol based on several numeral string datasets, including one composed of historical documents, has shown that the proposed method is a feasible end-to-end solution for numeral string recognition. Besides, it reduces the complexity of the string recognition task considerably since it drops out classical steps, in special preprocessing, segmentation, and a set of classifiers devoted to strings with a specific length.

Dynamic selection techniques aim at selecting the local experts around each test sample in particular for performing its classification. While generating the classifier on a local scope may make it easier for singling out the locally competent ones, as in the online local pool (OLP) technique, using the same base-classifier model in uneven distributions may restrict the local level of competence, since each region may have a data distribution that favors one model over the others. Thus, we propose in this work a problem-independent dynamic base-classifier model recommendation for the OLP technique, which uses information regarding the behavior of a portfolio of models over the samples of different problems to recommend one (or several) of them in a per-instance manner. Our proposed framework builds a multi-label meta-classifier responsible for recommending a set of relevant base-classifier models based on the local data complexity of the region surrounding each test sample. The OLP technique then produces a local pool with the model that yields the highest probability score of the meta-classifier. Experimental results show that different data distributions favored different model types on a local scope. Moreover, based on the performance of an ideal model type selector, it was observed that there is a clear advantage in choosing a relevant base-classifier model for each test instance in particular. Overall, the proposed model type recommender system yielded a statistically similar performance to the original OLP with fixed base-classifier model. However, the proposed framework struggled to recommend at least one relevant model type specially for the samples with low labelset cardinality. Given the novelty of the approach and the gap in performance between the proposed framework and the ideal selector, we regard this as a promising research direction. Code available at github.com/marianaasouza/dynamic-model-recommender.

Abstract Ensemble of classifiers can improve classification accuracy by combining several models. The fusion method plays an important role in the ensemble performance. Usually, a criterion for weighting the decision of each ensemble member is adopted. Frequently, this can be done using some heuristic based on accuracy or confidence. Then, the used fusion rule must consider the established criterion for providing a most reliable ensemble output through a kind of competition among the ensemble members. This article presents a new ensemble fusion method, named centrality score‐based fusion, which uses the centrality concept in the context of social network analysis (SNA) as a criterion for the ensemble decision. Centrality measures have been applied in the SNA to measure the importance of each person inside of a social network, taking into account the relationship of each person with all others. Thus, the idea is to derive the classifier weight considering the overall classifier prominence inside the ensemble network, which reflects the relationships among pairs of classifiers. We hypothesized that the prominent position of a classifier based on its pairwise relationship with the other ensemble members could be its weight in the fusion process. A robust experimental protocol has confirmed that centrality measures represent a promising strategy to weight the classifiers of an ensemble, showing that the proposed fusion method performed well against the literature.

DESlib is an open-source python library providing the implementation of several dynamic selection techniques. The library is divided into three modules: (i) dcs, containing the implementation of dynamic classifier selection methods (DCS); (ii) des, containing the implementation of dynamic ensemble selection methods (DES); (iii) static, with the implementation of static ensemble techniques. The library is fully documented (documentation available online on Read the Docs), has a high test coverage (codecov.io) and is part of the scikit-learn-contrib supported projects. Documentation, code and examples can be found on its GitHub page: https://github.com/scikit-learn-contrib/DESlib.

Offline Handwritten Signature verification presents a challenging Pattern Recognition problem, where only knowledge of the positive class is available for training. While classifiers have access to a few genuine signatures for training, during generalization they also need to discriminate forgeries. This is particularly challenging for skilled forgeries, where a forger practices imitating the user's signature, and often is able to create forgeries visually close to the original signatures. Most work in the literature address this issue by training for a surrogate objective: discriminating genuine signatures of a user and random forgeries (signatures from other users). In this work, we propose a solution for this problem based on meta-learning, where there are two levels of learning: a task-level (where a task is to learn a classifier for a given user) and a meta-level (learning across tasks). In particular, the meta-learner guides the adaptation (learning) of a classifier for each user, which is a lightweight operation that only requires genuine signatures. The meta-learning procedure learns what is common for the classification across different users. In a scenario where skilled forgeries from a subset of users are available, the meta-learner can guide classifiers to be discriminative of skilled forgeries even if the classifiers themselves do not use skilled forgeries for learning. Experiments conducted on the GPDS-960 dataset show improved performance compared to Writer-Independent systems, and achieve results comparable to state-of-the-art Writer-Dependent systems in the regime of few samples per user (5 reference signatures).

Research on adversarial examples in computer vision tasks has shown that small, often imperceptible changes to an image can induce misclassification, which has security implications for a wide range of image processing systems. Considering L2 norm distortions, the Carlini and Wagner attack is presently the most effective white-box attack in the literature. However, this method is slow since it performs a line-search for one of the optimization terms, and often requires thousands of iterations. In this paper, an efficient approach is proposed to generate gradient-based attacks that induce misclassifications with low L2 norm, by decoupling the direction and the norm of the adversarial perturbation that is added to the image. Experiments conducted on the MNIST, CIFAR-10 and ImageNet datasets indicate that our attack achieves comparable results to the state-of-the-art (in terms of L2 norm) with considerably fewer iterations (as few as 100 iterations), which opens the possibility of using these attacks for adversarial training. Models trained with our attack achieve state-of-the-art robustness against white-box gradient-based L2 attacks on the MNIST and CIFAR-10 datasets, outperforming the Madry defense when the attacks are limited to a maximum norm.

We propose an online method for concept drift detection based on dynamic classifier ensemble selection. The proposed method generates a pool of ensembles by promoting diversity among classifier members and chooses expert ensembles according to global prequential accuracy values. Unlike current dynamic ensemble selection approaches that use only local knowledge to select the most competent ensemble for each instance, our method focuses on selection taking into account the decision space. Consequently, it is well adapted to the context of drift detection in data stream problems. The results of the experiments show that the proposed method attained the highest detection precision and the lowest number of false alarms, besides competitive classification accuracy rates, in artificial datasets representing different types of drifts. Moreover, it outperformed baselines in different real-problem datasets in terms of classification accuracy.

Imbalanced problems are characterized by a disproportion between the number of samples from the classes in a classification problem. This difference in amount of examples may lead to a bias toward the majority class, hindering the recognition of the underrepresented minority class. Ensemble methods have been widely used for dealing with such problems, and have been shown to perform well on them. In this context, Dynamic Selection (DS) approaches, which perform the classification task on a local level, have been receiving some attention for their promising results. More specifically, the Frienemy Indecision Region Dynamic Ensemble Selection++ (FIRE-DES++) framework, which has yielded state-of-the-art results on imbalanced problems, use a data preprocessing technique for noise removal and a class-balanced neighborhood definition for coping with imbalanced datasets. A different DS-based approach proposed in a previous work, an online local pool generation method, generates on the fly locally accurate classifiers for labelling samples near the class borders. Though the local generation of the classifiers may reduce the impact of class imbalance on the performance of the technique, its suitability for imbalance learning was not yet evaluated. Thus, in this work we evaluate how well the online local pool generation method deals with imbalanced problems. We perform a comparative analysis with a baseline technique using three Dynamic Classifier Selection (DCS) techniques over 64 imbalanced datasets and four performance measures. We also evaluate the use of the preprocessing and balanced neighborhood definition steps from the FIRE-DES++ on the online scheme to assess their impact on the performance of the method. Moreover, we evaluate the online technique and its variants against seven state-of-the-art ensemble methods, including both static and DS approaches. Experimental results show that the approach of locally generating the classifiers is advantageous for imbalance learning, providing an improvement to the DCS techniques and yielding state-of-the-art results. Furthermore, the addition of the noise removal and the balanced neighborhood definition steps to the online scheme improved the overall results of the technique, which indicates the advantage of including such steps in DS-based techniques.

When compared to Writer-Dependent (WD) Handwritten Signature Verification, in which a model is trained for each individual writer, the Writer-Independent (WI) approach offers greater scalability, since only a single model is trained for all users from a dissimilarity space generated by the dichotomy transformation. However, many samples from the dissimilarity space are redundant and have little influence during the training of the verification model. This work investigates whether prototype selection (PS) preprocessing can be used in the space resulting from the dichotomy transformation without degrading the performance of the classifier. Furthermore, an investigation is also performed to examine the use of a WI classifier in a transfer learning scenario, i.e., where the classifier is trained in one dataset, and is used to verify signatures in other datasets. The experiments reported herein show that the use of prototype selection in the dissimilarity space allows a reduction in the complexity of the classifier without degrading its generalization performance. In addition, the results show that the WI classifier is scalable enough to be used in a transfer learning approach, with a resulting performance comparable to that of a classifier trained and tested in the same dataset. An analysis of the results obtained based on the instance hardness (IH) measure and dendrogram diagrams is performed in order to better understand the behavior of the resulting dichotomy transformation.

Class imbalance refers to classification problems in which many more instances are available for certain classes than for others. Such imbalanced datasets require special attention because traditional classifiers generally favor the majority class which has a large number of instances. Ensemble of classifiers has been reported to yield promising results. However, the majority of ensemble methods applied to imbalance learning are static ones. Moreover, they only deal with binary imbalanced problems. Hence, this paper presents an empirical analysis of Dynamic Selection techniques and data preprocessing methods for dealing with multi-class imbalanced problems. We considered five variations of preprocessing methods and 14 Dynamic Selection schemes. Our experiments conducted on 26 multi-class imbalanced problems show that the dynamic ensemble improves the AUC and the [Formula: see text]-mean as compared to the static ensemble. Moreover, data preprocessing plays an important role in such cases.

The phenomenon of adversarial examples is attracting increasing interest from the machine learning community, due to its significant impact on the security of machine learning systems. Adversarial examples are similar (from a perceptual notion of similarity) to samples from the data distribution, that “fool” a machine learning classifier. For computer vision applications, these are images with carefully crafted but almost imperceptible changes, which are misclassified. In this paper, we characterize this phenomenon under an existing taxonomy of threats to biometric systems, in particular identifying new attacks for offline handwritten signature verification systems. We conducted an extensive set of experiments on four widely used datasets: MCYT-75, CEDAR, GPDS-160, and the Brazilian PUC-PR, considering both a CNN-based system and a system using a handcrafted feature extractor. We found that attacks that aim to get a genuine signature rejected are easy to generate, even in a limited knowledge scenario, where the attacker does not have access to the trained classifier nor the signatures used for training. Attacks that get a forgery to be accepted are harder to produce, and often require a higher level of noise-in most cases, no longer “imperceptible” as previous findings in object recognition. We also evaluated the impact of two countermeasures on the success rate of the attacks and the amount of noise required for generating successful attacks.

Recognition of forest species is a very challenging task thanks to the great intra-class variability. To cope with such a variability, we propose a multiple classifier system based on a two-level classification strategy and microscopic images. By using a divide-and-conquer approach, an image is first divided into several sub-images which are classified independently by each classifier. In a first fusion level, partial decisions for the sub-images are combined to generate a new partial decision for the original image. Then, the second fusion level combines all these new partial decisions to produce the final classification of the original image. To generate the pool of diverse classifiers, we used classical texture-based features as well as keypoint-based features. A series of experiments shows that the proposed strategy achieves compelling results. Compared to the best single classifier, a Support Vector Machine (SVM) trained with a keypoint based feature set, the divide-and-conquer strategy improves the recognition rate in about 4 and 6 percentage points in the first and second fusion levels, respectively. The best recognition rate achieved by this proposed method is 98.47%.

This work describes a new oracle based Dynamic Ensemble Selection (DES) method in which an Ensemble of Classifiers (EoC) is selected to predict the class of a given test instance (xt). The competence of each classifier is estimated on a local region (LR) of the feature space (Region of Competence - RoC) represented by the most promising k-nearest neighbors (or advisors) related to xt according to a discrimination index (D) originally proposed in the Item and Test Analysis (ITA) theory. The D value is used to better define the advisors of the RoC since they will suggest the classifiers (local oracles) to compose the EoC. A robust experimental protocol based on 30 classification problems and 20 replications have shown that the proposed DES compares favorably with 15 state-of-the-art dynamic selection methods and the combination of all classifiers in the pool.

This paper presents the Centrality Based Fusion (CBF) method for ensemble fusion which is based on the centrality measures in the context of complex network theory. Such a concept has been applied in Social Network Analysis to measure the importance of each person inside of a social network. We hypothesized that the centrality of each classifier inside of an ensemble represented as a complex network could be combined with accuracy to provide the weight for its decision during the ensemble fusion. The main idea is to derive the weight considering the classifier importance inside the ensemble network which reflects the classifiers' diversity. A robust experimental protocol based on 30 datasets has confirmed that the notion of prominence provided employing centrality measures is a promising strategy to weight the classifiers of an ensemble. When compared with 9 fusion methods of the literature, the proposed fusion method won in 189 out of 270 experiments (70%), lost in 61 cases (22.59%) and tied in 20 cases (7.41%).

This paper presents segmentation-free strategies for the recognition of handwritten numeral strings of unknown length. A synthetic dataset of touching numeral strings of sizes 2-, 3- and 4-digits was created to train end-to-end solutions based on Convolutional Neural Networks. A robust experimental protocol is used to show that the proposed segmentation-free methods may reach the state-of-the-art performance without suffering the heavy burden of over-segmentation based methods. In addition, they confirmed the importance of introducing contextual information in the design of end-to-end solutions, such as the proposed length classifier when recognizing numeral strings.

Dynamic Ensemble Selection (DES) techniques aim to select locally competent classifiers for the classification of each new test sample. Most DES techniques estimate the competence of classifiers using a given criterion over the region of competence of the test sample (its the nearest neighbors in the validation set). The K-Nearest Oracles Eliminate (KNORA-E) DES selects all classifiers that correctly classify all samples in the region of competence of the test sample, if such classifier exists, otherwise, it removes from the region of competence the sample that is furthest from the test sample, and the process repeats. When the region of competence has samples of different classes, KNORAE can reduce the region of competence in such a way that only samples of a single class remain in the region of competence, leading to the selection of locally incompetent classifiers that classify all samples in the region of competence as being from the same class. In this paper, we propose two DES techniques: KNearest Oracles Borderline (KNORA-B) and K-Nearest Oracles Borderline Imbalanced (KNORA-BI). KNORA-B is a DES technique based on KNORA-E that reduces the region of competence but maintains at least one sample from each class that is in the original region of competence. KNORA-BI is a variation of KNORA-B for imbalance datasets that reduces the region of competence but maintains at least one minority class sample if there is any in the original region of competence. Experiments are conducted comparing the proposed techniques with 19 DES techniques from the literature using 40 datasets. The results show that the proposed techniques achieved interesting results, with KNORA-BI outperforming state-of-art techniques.

In Machine Learning, ensemble methods have been receiving a great deal of attention. Techniques such as Bagging and Boosting have been successfully applied to a variety of problems. Nevertheless, such techniques are still susceptible to the effects of noise and outliers in the training data. We propose a new method for the generation of pools of classifiers based on Bagging, in which the probability of an instance being selected during the resampling process is inversely proportional to its instance hardness, which can be understood as the likelihood of an instance being misclassified, regardless of the choice of classifier. The goal of the proposed method is to remove noisy data without sacrificing the hard instances which are likely to be found on class boundaries. We evaluate the performance of the method in nineteen public data sets, and compare it to the performance of the Bagging and Random Subspace algorithms. Our experiments show that in high noise scenarios the accuracy of our method is significantly better than that of Bagging.

In dynamic selection (DS) techniques, only the most competent classifiers, for the classification of a specific test sample are selected to predict the sample's class labels. The more important step in DES techniques is estimating the competence of the base classifiers for the classification of each specific test sample. The classifiers' competence is usually estimated using the neighborhood of the test sample defined on the validation samples, called the region of competence. Thus, the performance of DS techniques is sensitive to the distribution of the validation set. In this paper, we evaluate six prototype selection techniques that work by editing the validation data in order to remove noise and redundant instances. Experiments conducted using several state-of-the-art DS techniques over 30 classification problems demonstrate that by using prototype selection techniques we can improve the classification accuracy of DS techniques and also significantly reduce the computational cost involved.

Hyperparameter optimization is now widely applied to tune the hyperparameters of learning algorithms. The hyperparameters can have structure, resulting in hyperparameters depending on conditions, or on the values of other hyperparameters. We target the problem of combined algorithm selection and hyperparameter optimization, which includes at least one conditional hyperparameter: the choice of the learning algorithm. In this work, we show that Bayesian optimization with Gaussian processes can be used for the optimization of conditional spaces with the injection of knowledge concerning conditions in the kernel. We propose and examine the behavior of two kernels, a conditional kernel which forces the similarity of two samples from different condition branches to be zero, and the Laplace kernel, based on similarities with Mondrian processes and random forests. We show the benefit of using such kernels, as well as proper imputation of inactive hyperparameters, on a benchmark of scikit-learn models.

This paper proposes a classification approach in which monolithic and multiple classifier systems are combined in a cascading fashion. The rationale behind that is to deal with the existing trade-off between the need for increasing the accuracy, while reducing the complexity of the classification method. In other words, the idea is to offer an interesting strategy to conciliate the different levels of efforts necessary to deal with easy and hard patterns usually observed in a classification problem. The experimental results have shown that for some problems more than 90% of the instances can be processed in the first step of the cascade, saving efforts by avoiding the use of the second step in which a more complex classification method is used. It means that for some problems the reduction of the classification cost achieved more than 70% when compared to the use of an MCS. In addition to this interesting classification cost reduction, the cascade approach has shown to be able of improving the classification accuracy up to 15.19 percentage points.

The Oracle model has been used not only for comparison between techniques but also in the design of different methods in Multiple Classifier Systems (MCS). Even though the model represents the ideal classifier selection scheme, Dynamic Classifier Selection (DCS) techniques present a large performance gap from the Oracle. This means that, for a significant number of instances, the DCS techniques are not able to select a competent classifier, despite the Oracles assurance of its presence in the pool. Given that issue, this work aims to investigate the reasons why the Oracle model may not be well suited for guiding the search for a promising pool of classifiers for DCS techniques. For this purpose, a pool generation method that guarantees an Oracle accuracy rate of 100% in the training set is proposed. This method is further used to analyse the behavior of DCS techniques when the presence of at least one competent classifier in the pool for each training sample is assured. Experiments show that integrating Oracle information in the generation phase of an MCS has little impact on the gap between the accuracy rates of DCS techniques and the Oracle. Moreover, it is also shown that, for a theoretical limit of 100%, the DCS techniques were only able to select a competent classifier for at most 85% of the instances, on average. Results suggest that the Oracle is not the best guide for generating a pool of classifiers for DCS, for the model is performed globally whilst DCS techniques work with local data only.

Biometric researchers have historically seen signature duplication as a procedure relevant to improving the performance of automatic signature verifiers. Different approaches have been proposed to duplicate dynamic signatures based on the heuristic affine transformation, nonlinear distortion and the kinematic model of the motor system. The literature on static signature duplication is limited and as far as we know based on heuristic affine transforms and does not seem to consider the recent advances in human behavior modeling of neuroscience. This paper tries to fill this gap by proposing a cognitive inspired algorithm to duplicate off-line signatures. The algorithm is based on a set of nonlinear and linear transformations which simulate the human spatial cognitive map and motor system intra-personal variability during the signing process. The duplicator is evaluated by increasing artificially a training sequence and verifying that the performance of four state-of-the-art off-line signature classifiers using two publicly databases have been improved on average as if we had collected three more real signatures.

Multi-script writer identification consists in identifying a person of a given text written in one script from the samples of the same person written in another script. The rationale behind this is that the writing style of an individual remains constant across different scripts. While this hypothesis may hold, recent results on a multi-script writer identification competition show that classical writer-dependent classifiers fail in this task. In this work we investigate the efficacy of a writer-independent classifier based on dissimilarity for multi-script writer identification. The classifiers were trained using two different texture descriptors (LBP and LPQ). Our experiments on 475 writers of the QUWI dataset, which is composed of Arabic and English samples, show that the proposed strategy surpasses the results published in the literature by a large margin, achieving error rates similar to single-script writer identification systems.

As an emerging issue, multi-script signature verification is a recent challenge for current Automatic Signature Verification (ASV) systems. Relevant differences are presented in the morphology and lexicon of the signature images written in different scripts, such as used symbols, shape of the signatures, legibility, etc. These peculiarities could reduce the success of ASV systems, especially those which were originally designed for only one kind of script. However, one common feature among scripts in ASV is the fact that the greater the number of signatures that are used for training, the better the expected performance. In this work, we propose a method inspired by observations from the neuromotor equivalence theory to artificially enlarge the signature images used to train a state-of-the-art static signature classifier. Experimental results are obtained by using three static signature datasets derived from completely different scripts: Western, Bengali and Devanagari. Our results suggest that the cognitive-inspired model, which aims to duplicate static signatures, tends toward intra-class variability of signatures written in different scripts; the model's beneficial impact is seen in signature verification tests.

In many real-world classification tasks, it is crucial to take into account misclassification costs for designing an accurate classification system. Nevertheless, begin able to reject a sample is also often needed in order to avoid a very risky prediction error. In that case, a cost-sensitive classifier must embed a rejection mechanism, that takes into account the rejection costs as well as the misclassification costs. In binary classification, the ROC space has shown to be very powerful for designing cost-sensitive classifiers, but it has been poorly exploited for designing classifiers able to reject. The purpose of this work is to extend a ROC-based ensemble method recently proposed, called the ROC Front method, with a cost-sensitive rejection mechanism. This approach compares favorably to the state-of-the-art ROC-based rejection rule recently proposed for binary cost-sensitive classification. It is also more robust as it allows to design an accurate classifier for all cost-sensitive situations contrary to the state-of-the-art method that fails in many cases, as for example with small datasets.

Research on Offline Handwritten Signature Verification explored a large variety of handcrafted feature extractors, ranging from graphology, texture descriptors to interest points. In spite of advancements in the last decades, performance of such systems is still far from optimal when we test the systems against skilled forgeries - signature forgeries that target a particular individual. In previous research, we proposed a formulation of the problem to learn features from data (signature images) in a Writer-Independent format, using Deep Convolutional Neural Networks (CNNs), seeking to improve performance on the task. In this research, we push further the performance of such method, exploring a range of architectures, and obtaining a large improvement in state-of-the-art performance on the GPDS dataset, the largest publicly available dataset on the task. In the GPDS-160 dataset, we obtained an Equal Error Rate of 2.74%, compared to 6.97% in the best result published in literature (that used a combination of multiple classifiers). We also present a visual analysis of the feature space learned by the model, and an analysis of the errors made by the classifier. Our analysis shows that the model is very effective in separating signatures that have a different global appearance, while being particularly vulnerable to forgeries that very closely resemble genuine signatures, even if their line quality is bad, which is the case of slowly-traced forgeries.

Dynamic selection (DS) is a mechanism to select one or an ensemble of competent classifiers from a pool of base classifiers, in order to classify a specific test sample. The size of this pool is user defined and yet crucial to control the computational complexity and performance of a DS. An appropriate pool size depends on the choice of base classifiers, the underlying DS method used, and more importantly, the characteristics of the given problem. After the DS method and the base classifiers are selected, an appropriate pool size for a given problem can be obtained by the repetitive application of the DS with a variety of sizes, after which a selection is performed. Since this brute force approach is computationally expensive, researchers usually set the pool size to a pre-specified value. However, this strategy may reduce the performance of the DS method. Instead, we propose a meta-regression model in order to predict a suitable pool size, based on the intrinsic classification complexity of a problem. In our strategy, we obtain the best pool sizes for a number of data sets, using the brute force approach. Additionally, we extract meta-features that represent classification complexity of a problem. These two pieces of information are associated by means of meta-regression models. Finally, for an unseen problem, we predict the pool size using this model and the classification complexity information.We carry out the experiments on 64 two-class data sets and with several well-known DS methods. We also consider variants of meta-regression techniques and report prediction results. We further analyze these results using a statistical test. Finally, we investigate the performance of a DS and observe that DS performs equivalently for predicted and the best pool sizes.

This work describes the Dynse framework, which uses dynamic selection of classifiers to deal with concept drift. Basically, classifiers trained on new supervised batches available over time are add to a pool, from which is elected a custom ensemble for each test instance during the classification time. The Dynse framework is highly customizable, and can be adapted to use any method for dynamic selection of classifiers given a test instance. In this work we propose a default configuration for the framework which has provided promising results in a range of problems. The experimental results have shown that the proposed framework achieved the best average rank when considering all datasets, and outperformed the state-of-the-art in three of four tested datasets.

Different dynamic classifier selection techniques have been proposed in the literature to determine among diverse classifiers available in a pool which should be used to classify a test instance. The individual competence of each classifier in the pool is usually evaluated taking into account its accuracy on the neighborhood of the test instance in a validation dataset. In this work we investigate the possible contribution of considering during the classifier evaluation the use of features related to the problem complexity. Since usually the pool generation technique does not assure diversity, the idea is to consider diversity during the selection. Basically, we select a classifier trained in subset of data showing similar complexity than that observed in neighborhood of the test instance. We expect that this similarity in terms of complexity allow us to select a more competent classifier. Experiments on 30 classification problems representing different levels of difficulty have shown that the proposed selection method is comparable to well known dynamic selection strategies. When compared with other DS approaches it was able to win on 123 over 150 experiments. This promising results indicate that further investigation must be done to increase diversity in terms of data complexity during the process of pool generation.

Automatic Offline Handwritten Signature Verification has been researched over the last few decades from several perspectives, using insights from graphology, computer vision, signal processing, among others. In spite of the advancements on the field, building classifiers that can separate between genuine signatures and skilled forgeries (forgeries made targeting a particular signature) is still hard. We propose approaching the problem from a feature learning perspective. Our hypothesis is that, in the absence of a good model of the data generation process, it is better to learn the features from data, instead of using hand-crafted features that have no resemblance to the signature generation process. To this end, we use Deep Convolutional Neural Networks to learn features in a writer-independent format, and use this model to obtain a feature representation on another set of users, where we train writer-dependent classifiers. We tested our method in two datasets: GPDS-960 and Brazilian PUC-PR. Our experimental results show that the features learned in a subset of the users are discriminative for the other users, including across different datasets, reaching close to the state-of-the-art in the GPDS dataset, and improving the state-of-the-art in the Brazilian PUC-PR dataset.

Systems for still-to-video face recognition (FR) seek to detect the presence of target individuals based on reference facial still images or mug-shots. These systems encounter several challenges in video surveillance applications due to variations in capture conditions (e.g., pose, scale, illumination, blur and expression) and to camera inter-operability. Beyond these issues, few reference stills are available during enrollment to design representative facial models of target individuals. Systems for still-to-video FR must therefore rely on adaptation, multiple face representation, or synthetic generation of reference stills to enhance the intra-class variability of face models . Moreover, many FR systems only match high quality faces captured in video, which further reduces the probability of detecting target individuals. Instead of matching faces captured through segmentation to reference stills, this paper exploits Adaptive Appearance Model Tracking (AAMT) to gradually learn a track-face-model for each individual appearing in the scene. The Sequential Karhunen–Loeve technique is used for online learning of these track-face-models within a particle filter-based face tracker. Meanwhile, these models are matched over successive frames against the reference still images of each target individual enrolled to the system, and then matching scores are accumulated over several frames for robust spatiotemporal recognition. A target individual is recognized if scores accumulated for a track-face-model over a fixed time surpass some decision threshold. The main advantage of AAMT over traditional still-to-video FR systems is the greater diversity of facial representation that may be captured during operations, and this can lead to better discrimination for spatiotemporal recognition. Compared to state-of-the-art adaptive biometric systems, the proposed method selects facial captures to update an individual׳s face model more reliably because it relies on information from tracking. Simulation results obtained with the Chokepoint video dataset indicate that the proposed method provides a significantly higher level of performance compared state-of-the-art systems when a single reference still per individual is available for matching. This higher level of performance is achieved when the diverse facial appearances that are captured in video through AAMT correspond to that of reference stills.

Recognizing the face of target individuals in a watch-list is among the most challenging applications in video surveillance, especially when enrollment is based on one reference still facial image. Besides the limited representativeness of facial models used for matching, the appearance of faces captured in videos varies due to changes in illumination, pose, scales, etc., and to camera inter-operability. A multi-classifier system is proposed in this paper for robust still-to-video face recognition (FR) based on multiple diverse face representations. An individual-specific ensemble of exemplar-SVMs (e-SVMs) classifiers is assigned to each target person, where each classifier is trained using a high-quality reference face still versus many lower-quality faces of non-target individuals captured in videos. Diverse face representations are generated from different patches isolated in facial images and face descriptors that are robust to various nuisance factors (e.g., illumination and pose) commonly encountered in surveillance environments. Discriminant feature subsets, training samples, and ensemble fusion functions are selected using faces of non-target individuals captured in videos of the scene. Experiments on videos from the Chokepoint dataset reveal that the proposed ensemble of e-SVMs outperforms state-of-the-art FR systems specialized for the single sample per person problem.

In Dynamic Ensemble Selection (DES) techniques, only the most competent classifiers are selected to classify a given query sample. Hence, the key issue in DES is how to estimate the competence of each classifier in a pool to select the most competent ones. In order to deal with this issue, we proposed a novel dynamic ensemble selection framework using meta-learning, called META-DES. The framework is divided into three steps. In the first step, the pool of classifiers is generated from the training data. In the second phase the meta-features are computed using the training data and used to train a meta-classifier that is able to predict whether or not a base classifier from the pool is competent enough to classify an input instance. In this paper, we propose improvements to the training and generalization phase of the META-DES framework. In the training phase, we evaluate four different algorithms for the training of the meta-classifier. For the generalization phase, three combination approaches are evaluated: Dynamic selection, where only the classifiers that attain a certain competence level are selected; Dynamic weighting, where the meta-classifier estimates the competence of each classifier in the pool, and the outputs of all classifiers in the pool are weighted based on their level of competence; and a hybrid approach, in which first an ensemble with the most competent classifiers is selected, after which the weights of the selected classifiers are estimated in order to be used in a weighted majority voting scheme. Experiments are carried out on 30 classification datasets. Experimental results demonstrate that the changes proposed in this paper significantly improve the recognition accuracy of the system in several datasets.

Adaptive classifier ensembles have been shown to improve the accuracy and robustness of systems for face recognition (FR) in video surveillance. However, it is often assumed that the proportions of faces captured for target and non-target individuals are balanced, or they are known a priori, and constant over time. Some active approaches have been proposed to update the ensemble during operations according to class imbalance of the input data stream. Beyond the estimation operational class imbalance, these approaches commonly generate diverse pools of classifiers by selecting balanced training data, limiting the potential diversity provided by the abundant non-target data. In this paper, a skew-sensitive ensemble is proposed to adaptively combine classifiers trained with data selected to have varying levels of imbalance and complexity. Given a face re-identification application, faces captured for each person appearing in the scene are tracked and regrouped into trajectories. During enrollment, faces in a reference trajectory are combined with those of selected non-target trajectories to generate a pool of 2-class classifiers using data with various levels of imbalance and complexity. During operations, the level of imbalance is periodically estimated by comparing input trajectories and pre-computed histograms using Hellinger distance quantification. Ensemble fusion functions are then adapted based on the imbalance and complexity of operational data. Finally, ensemble scores are accumulated over trajectories for robust spatio-temporal FR. Results obtained in experiments with synthetic data and Face in Action videos reveal that the proposed approach can significantly improve performance across operational imbalances.

Convolutional Neural Networks (CNNs) have set the state-of-the-art in many computer vision tasks in recent years. For this type of model, it is common to have millions of parameters to train, commonly requiring large datasets. We investigate a method to transfer learning across different texture classification problems, using CNNs, in order to take advantage of this type of architecture to problems with smaller datasets. We use a Convolutional Neural Network trained on a source dataset (with lots of data) to project the data of a target dataset (with limited data) onto another feature space, and then train a classifier on top of this new representation. Our experiments show that this technique can achieve good results in tasks with small datasets, by leveraging knowledge learned from tasks with larger datasets. Testing the method on the the Brodatz-32 dataset, we achieved an accuracy of 97.04% - superior to models trained with popular texture descriptors, such as Local Binary Patterns and Gabor Filters, and increasing the accuracy by 6 percentage points compared to a CNN trained directly on the Brodatz-32 dataset. We also present a visual analysis of the projected dataset, showing that the data is projected to a space where samples from the same class are clustered together - suggesting that the features learned by the CNN in the source task are relevant for the target task.

This paper presents a k-nearest oracle-based dynamic ensemble selection method in which overall local accuracy (OLA) and local class accuracy (LCA) are combined into a twostep selection scheme. The OLA and LCA are computed on the neighborhood of the test pattern in a validation set to filter out the classifiers selected by the k-nearest oracles. The complementary information of OLA and LCA has shown to be an interesting alternative to approximate the classification performance to that estimated for the oracle of the initial pool of classifiers. The results were compared with the recognition rates of the majority voting of all classifiers in the initial pool, and also with the recognition rates of related classifier and ensemble selection methods which have inspired the proposed method and its variants. The proposed method achieved the best results on 5 out of 8 experiments using small and large datasets of different applications.

During video surveillance, face re‐identification allows recognition and targeting of individuals of interest from faces captured across a network of video cameras. In such applications, face recognition is challenging because faces are captured under limited spatial and temporal constraints. In addition, facial models for recognition are commonly designed using a limited number of representative reference samples from faces captured under specific conditions, regrouped into facial trajectories. Given new reference samples (provided by an operator or through some self‐updating process), updating facial models may allow maintenance of a high level of performance over time. Although adaptive ensembles have been successfully applied to robust modelling of an individual's facial appearance, reference data samples from a trajectory must be stored for validation. In this study, a memory management strategy based on Kullback–Leiber (KL) divergence is proposed to rank and select the most relevant validation samples over time in adaptive individual‐specific ensembles. When new reference samples become available for an individual, updates to the corresponding ensemble are validated using a mixture of new and previously‐stored samples. Only the samples with the highest KL divergence are preserved in memory for future adaptations. This strategy is compared with reference classifiers using videos from the face in action data. Simulation results show that the proposed strategy tends to select discriminative samples from wolf‐like individuals for validation. It allows maintenance of a high level of performance, while reducing the number of samples per individual by up to 80%.

Biometric cryptosystems have been applied to secure secret keys for encryption and digital signatures by means of biometric traits, e.g., Fingerprint, face, etc., where the fuzzy vault (FV) mechanism has been extensively employed. Recently, the authors proposed a FV system based on the offline signature images, so that digitized documents can be secured with the embedded handwritten signatures. However, the FV design concerns mostly with alleviating biometric variability with less focusing on its power in discriminating forgeries. Accordingly, the decoding accuracy of implementations is below the level required in practical banking transactions. On the other hand, signature verification (SV) systems have shown higher accuracy in discriminating forgeries. In this paper, accuracy of signature-based biometric cryptosystems is enhanced by cascading SV and FV modules. Signature samples are first verified by the SV module. Then, only verified samples are processed by FV decoders for unlocking cryptographic keys. Hence, the upper limit of the false accept rate is determined by the more accurate SV module. Simulation results obtained with the Brazilian signature database indicate the viability of the proposed approach. Cascaded SV-FV system increases decoding accuracy by about 35% compared to the pure FV systems.

Still-to-video face recognition (FR) is an important function in watch list screening, where faces captured over a network of video surveillance cameras are matched against reference stills of target individuals. Recognizing faces in a watch list is a challenging problem in semi -- and unconstrained surveillance environments due to the lack of control over capture and operational conditions, and to the limited number of reference stills. This paper provides a performance baseline and guidelines for ensemble-based systems using a single high-quality reference still per individual, as found in many watch list screening applications. In particular, modular systems are considered, where an ensemble of template matchers based on multiple face representations is assigned to each individual of interest. During enrollment, multiple feature extraction (FE) techniques are applied to patches isolated in the reference still to generate diverse face-part representations that are robust to various nuisance factors (e.g., illumination and pose) encountered in video surveillance. The selection of relevant feature subsets, decision thresholds, and fusion functions of ensembles are achieved using faces of non-target individuals selected from reference videos (forming a universal background model). During operations, a face tracker gradually regroups faces captured from different people appearing in a scene, while each user-specific ensemble generates a decision per face capture. This leads to robust spatio-temporal FR when accumulated ensemble predictions surpass a detection threshold. Simulation results obtained with the Chokepoint video dataset show a significant improvement to accuracy, (1) when performing score-level fusion of matchers, where patches-based and FE techniques generate ensemble diversity, (2) when defining feature subsets and decision thresholds for each individual matcher of an ensemble using non-target videos, and (3) when accumulating positive detections over multiple frames.

In this study we assess the performance of textural descriptors for writer identification on different writing styles and also on forgeries. To do that, we have performed a series of experiments using the Fire maker database, which provides for the same writer texts written on three different writing styles and also copied forged text. Our experimental protocol is based on the dissimilarity framework and SVM classifiers, which were trained with LBP (Local Binary Pattern) and LPQ (Local Phase Quantization). The 250 writers of the database were divided into different configurations to observe the impacts of different sizes of the training set on the performance of the system. Our experimental results corroborates the fact that the texture is an interesting alternative for writer identification. The classifier trained with LPQ was able to produce error rates 23 percentage points smaller than those reported in the literature for upper-case and free writing styles. Regarding the forgeries, the LPQ-based classifier goes further reducing the error rate up to 44 percentage points depending on the writing style used for training.

In this paper, we propose a novel dynamic ensemble selection framework using meta-learning. The framework is divided into three steps. In the first step, the pool of classifiers is generated from the training data. The second phase is responsible to extract the meta-features and train the meta-classifier. Five distinct sets of meta-features are proposed, each one corresponding to a different criterion to measure the level of competence of a classifier for the classification of a given query sample. The meta-features are computed using the training data and used to train a meta-classifier that is able to predict whether or not a base classifier from the pool is competent enough to classify an input instance. Three different training scenarios for the training of the meta-classifier are considered: problem-dependent, problem-independent and hybrid. Experimental results show that the problem-dependent scenario provides the best result. In addition, the performance of the problem-dependent scenario is strongly correlated with the recognition rate of the system. A comparison with state-of-the-art techniques shows that the proposed-dependent approach outperforms current dynamic ensemble selection techniques.

For applications of face recognition (FR) in video surveillance, it is often costly or unfeasible to collect several high quality reference samples a priori to design representative facial models. Moreover, changes in capture conditions and human physiology create divergence between facial models and input captures. Multiple classifier systems (MCS) have been successfully applied to video-to-video FR, where the face of each individual of interest is modeled using an ensemble of 2-class classifiers (trained on target vs. non-target samples). However, the reliable self-update of these individual-specific ensembles with relevant target and non-target samples raises several challenges. In this paper, an adaptive MCS is proposed that allows for self-updating facial models given face trajectories captured during operations. Different faces appearing in a camera viewpoint are tracked, and ensemble predictions for facial captures are accumulated along each track for robust video-to-video FR. When the number of positive predictions over time surpasses an update threshold, the target face samples extracted from the trajectory are combined with non-target samples selected from the cohort and universal models for efficient self-update the corresponding face model. A learn-and-combine strategy is then employed to avoid knowledge corruption during self-update of an ensemble. At a transaction level, the adaptive MCS outperforms the reference systems that do not allow self-updating on Face in Action videos. Analysis at a trajectory level indicates that the proposed system allows for robust spatio-temporal recognition, which translates to enhanced security and situation analysis.

Video-based face screening is essentially a detection problem where faces captured in video sequences are matched against the facial models of individuals of interest. This problem is associated with several operational challenges, from lighting and pose changes, to natural aging of target individuals, and to the limited availability of reference samples from changing environments to design facial models. Some matchers proposed in literature may be employed to adapt facial models of individuals enrolled to the system in response to new reference samples. This paper reviews and compares the performance of these matchers, focusing on their ability for adapting to new data. An experimental methodology is proposed to assess their performance for video surveillance applications. This methodology is focused on transactional and subject-based performance, and considers the imbalance of positive and negative samples. Experiments are then performed with the Canegie Mellon University Face in Action video dataset, according to matching accuracy and resource requirements. Results indicate that ensemble-based matchers outperform traditional monolithic approaches, maintaining a higher level of accuracy over time when adapting to new reference samples.

In this paper, we propose a novel approach for the multi-objective optimization of classifier ensembles in the ROC space. We first evolve a pool of simple classifiers with NSGA-II using values of the ROC curves as the optimization objectives. These simple classifiers are then combined at the decision level using the Iterative Boolean Combination method (IBC). This method produces multiple ensembles of classifiers optimized for various operating conditions. We perform a rigorous series of experiments to demonstrate the properties and behaviour of this approach. This allows us to propose interesting venues for future research on optimizing ensembles of classifiers using multi-objective evolutionary algorithms.

In dynamic optimization problems, the optima location and fitness value change over time. Techniques in literature for dynamic optimization involve tracking one or more peaks moving in a sequential manner through the parameter space. However, many practical applications in, e.g., video and image processing involve optimizing a stream of recurrent problems, subject to noise. In such cases, rather than tracking one or more moving peaks, the focus is on managing a memory of solutions along with information allowing to associate these solutions with their respective problem instances. In this paper, Gaussian Mixture Modeling (GMM) of Dynamic Particle Swarm Optimization (DPSO) solutions is proposed for fast optimization of streams of recurrent problems. In order to avoid costly re-optimizations over time, a compact density representation of previously-found DPSO solutions is created through mixture modeling in the optimization space, and stored in memory. For proof of concept simulation, the proposed hybrid GMM-DPSO technique is employed to optimize embedding parameters of a bi-tonal watermarking system on a heterogeneous database of document images. Results indicate that the computational burden of this watermarking problem is reduced by up to 90.4% with negligible impact on accuracy.

Although writer-independent offline signature verification (WI-SV) systems may provide a high level of accuracy, they are not secure due to the need to store user templates for authentication. Moreover, state-of-the-art writer-dependent (WD) and writer-independent (WI) systems provide enhanced accuracy through information fusion at either feature, score or decision levels, but they increase computational complexity. In this paper, a method for adapting WI-SV systems to different users is proposed, leading to secure and compact WD-SV systems. Feature representations embedded within WI classifiers are extracted and tuned to each enrolled user while building a user-specific classifier. Simulation results on the Brazilian signature database indicate that the proposed method yields WD classifiers that provide the same level of accuracy as that of the baseline WI classifiers (AER of about 5.38), while reducing complexity by about 99.5%.

Automatic bearing fault diagnosis may be approached as a pattern recognition problem that allows for a significant reduction in the maintenance costs of rotating machines, as well as the early detection of potentially disastrous faults. When these systems employ real vibration data obtained from bearings artificially damaged, they have to cope with a very limited number of training samples. Moreover, an important issue that has been little investigated in the literature is the presence of noise, which disturbs the vibration signals, and how this affects the identification of bearing defects. In this paper, a new strategy based on the fusion of different Support Vector Machines (SVM) is proposed in order to reduce noise effect in bearing fault diagnosis systems. Each SVM classifier is designed to deal with a specific noise configuration and, when combined together - by means of the Iterative Boolean Combination (IBC) technique - they provide high robustness to different noise-to-signal ratio. In order to produce a high amount of vibration signals, considering different defect dimensions and noise levels, the BEAring Toolbox (BEAT) is employed in this work. Experiments indicate that the proposed strategy can significantly reduce the error rates, even in the presence of very noisy signals.

In this work we assess the recently introduced Local Phase Quantization (LPQ) as textural descriptor for the problem of forest species recognition. LPQ is based on quantizing the Fourier transform phase in local neighborhoods and the phase can be shown to be a blur invariant property under certain commonly fulfilled conditions. We show through a series of comprehensive experiments that LPQ surpasses the results achieved by the widely used Local Binary Patterns (LPB) and its variants. Our experiments also show, though, that the results can be further improved by combining both LPB and LPQ. In this sense, several different combination strategies were tried out. Using a SVM classifiers, the combination of LPB and LPQ brought an improvement of about 7 percentage points on a database composed by 2,240 microscopic images extracted from 112 different forest species.

Video-based face recognition of individuals involves matching facial regions captured in video sequences against the model of individuals enrolled to a face recognition system. Due to a limited control over operational conditions, classification systems applied to face matching are confronted with complex pattern recognition environments that change over time. Therefore, the facial model of an individual tends to diverge from the underlying data distribution. Although a limited amount of reference data is often collected during initial enrollment, new samples often become available over time to update and refine models. In this paper, an adaptive ensemble of classifiers is proposed to update facial models in response to new reference samples. To avoid knowledge corruption linked to incremental learning of monolithic classifiers, and maintain a high level of performance, this ensemble exploits a learn-and-combine approach. In response to new reference samples, a new 2-class Probabilistic Fuzzy ARTMAP classifier is trained and combined to previously-trained classifiers in the ROC space. Iterative Boolean Combination is employed for fusion of 2-class classifiers of each individual in the decision space. Performance is assessed in terms of AUC accuracy and resource requirements under different incremental learning scenarios with new data extracted from the Faces in Action data set. Simulation results indicate that the proposed system significantly outperforms reference classifiers and ensembles for incremental learning.

Biometric systems for recognizing faces in video streams have become relevant in a growing number of private and public sector applications, among them screening for individuals of interest in dense and moving crowds. In practice, the performance of these systems typically declines because they encounter a variety of uncontrolled conditions that change during operations, and they are designed a priori using limited data and knowledge of underlying data distributions. This paper presents multi-classifier system that can achieve a high level of performance in real-world video surveillance applications. This system assigns an ensemble of detectors (2-class classifiers) per individual, where base detectors are co-jointly trained using population-based evolutionary optimization. During enrolment of an individual, an aggregative Dynamic Niching Particle Swarm Optimization (DNPSO)-based training strategy generates a diversified homogenous pool of ARTMAP neural network classifiers using reference data samples. Classifiers associated with local optima of the aggregative DNPSO are directly selected and efficiently combined in the Receiver Operating Characteristic (ROC) space. Performance is assessed in terms of both accuracy and resource requirements on facial regions extracted from video streams of the Face in Action database. A comparison between a standard global and modular classification architectures is provided in this paper. Simulation results indicate that recognizing an individual using the aforementioned ensemble of detectors provides a scalable architecture that maintains a significantly higher level of accuracy and robustness as the number of individuals grows.

The exploration/exploitation balance is a major concern in the control of evolutionary algorithms (EAs) performance. Exploration is associated with the distribution of individuals on a landscape, and can be estimated by a genotypic diversity measure (GDM). In contrast, exploitation is related to individual responses, which can be described with a phenotypic diversity measure. Many diversity measures have been proposed in the literature without a comprehensive study of their differences. This paper looks at surveys of GDMs published over the years for real-coded representations, and compares them based on a new benchmark, one that allows a better description of their behavior. The results demonstrate that none of the available GDMs is able to reflect the true diversity of all search processes. Nonetheless, the normalized pairwise diversity measurement proves to be the best genotypic diversity measurement for standard EAs, as it shows nondominated behavior with respect to the desired GDM requirements.

Dynamic classifier ensemble selection is focused on selecting the most confident classifier ensemble to predict the class of a particular test pattern. The overproduce-and-choose strategy is a dynamic classifier ensemble selection method which is divided into optimization and dynamic selection phases. The first phase involves the test of different candidate ensembles in order to produce a population composed of the highest performing candidate ensembles. Then, the second phase calculates the domain of expertise of each candidate ensemble to pick up the solution with highest degree of certainty of its decision to classify the unknown test samples. It has been shown that the optimization phase decreases oracle, the upper bound of dynamic selection processes. In this paper we propose a hybrid algorithm to perform the optimization phase of overproduce and-choose strategy. The proposed algorithm combines stochastic initialization of candidate ensembles of different sizes, with the traditional forward search greedy method. The objective is to apply oracle as search criterion during the optimization phase. We show experimentally that choosing the population of classifier ensembles taking into account the population oracle leads to increase the upper bound of the dynamic selection phase. Moreover, experimental results conducted to compare the proposed method to a multi-objective genetic algorithm (MOGA), demonstrate that our method outperforms MOGA on generating population of candidate ensembles with higher oracle rates.

A fundamental problem when performing incremental learning is that the best set of a classification system's parameters can change with the evolution of the data. Consequently, unless the system self-adapts to such changes, it will become obsolete, even if the application environment seems to be static. To address this problem, we propose a dynamic optimization approach in this paper that performs incremental learning in an adaptive fashion by tracking, evolving, and combining optimum hypotheses overtime. The approach incorporates various theories, such as dynamic particle swarm optimization, incremental support vector machine classifiers, change detection, and dynamic ensemble selection based on classifiers' confidence levels. Experiments carried out on synthetic and real-world databases demonstrate that the proposed approach actually outperforms the classification methods often used in incremental learning scenarios. © 2011 Wiley Periodicals, Inc.

A key feature in population based optimization algorithms is the ability to explore a search space and make a decision based on multiple solutions. In this paper, an incremental learning strategy based on a dynamic particle swarm optimization (DPSO) algorithm allows to produce heterogeneous ensembles of classifiers for video-based face recognition. This strategy is applied to an adaptive classification system (ACS) comprised of a swarm of fuzzy ARTMAP (FAM) neural network classifiers, a DPSO algorithm, and a long term memory (LTM). The performance of this ACS with an ensemble of FAM networks selected among local bests of the swarm, is compared to that of the ACS with the global best network under different incremental learning scenarios. Performance is assessed in terms of classification rate and resource requirements for incremental learning of new data blocks extracted from real-world video streams, and are given along with reference kNN and FAM classifier optimized for batch learning. Simulation results indicate that the learning strategy maintains diversity within the ensemble classifiers, providing a significantly higher classification rate than that of the best FAM network alone. However, classification with an ensemble requires more resources.

In this paper, a two-stage off-line signature verification system based on dissimilarity representation is proposed. In the first stage, a set of discrete left-to-right HMMs trained with different number of states and codebook sizes is used to measure similarity values that populate new feature vectors. Then, these vectors are input to the second stage, which provides the final classification. Experiments were performed using two different classification techniques - AdaBoost, and Random Subspaces with SVMs - and a real-world signature verification database. Results indicate that the performance is significantly better with the proposed system over other reference signature verification systems from literature.

The incremental updating of classifiers implies that their internal parameter values can vary according to incoming data. As a result, in order to achieve high performance, incremental learner systems should not only consider the integration of knowledge from new data, but also maintain an optimum set of parameters. In this paper, we propose an approach for performing incremental learning in an adaptive fashion with an ensemble of support vector machines. The key idea is to track, evolve, and combine optimum hypotheses over time, based on dynamic optimization processes and ensemble selection. From experimental results, we demonstrate that the proposed strategy is promising, since it outperforms a single classifier variant of the proposed approach and other classification methods often used for incremental learning.

Using Boolean AND and OR functions to combine the responses of multiple one- or two-class classifiers in the ROC space may significantly improve performance of a detection system over a single best classifier. However, techniques found in literature assume that the classifiers are conditionally independent, and that their ROC curves are convex. These assumptions are not valid in most real-world applications, where classifiers are designed using limited and imbalanced training data. A new Iterative Boolean Combination (IBC) technique applies all Boolean functions to combine the ROC curves produced by multiple classifiers without prior assumptions, and its time complexity is linear according to the number of classifiers. The results of computer simulations conducted on synthetic and real-world host-based intrusion detection data indicate that combining the responses from multiple HMMs with IBC can achieve a significantly higher level of performance than with the AND and OR combinations, especially when training data is limited and imbalanced.

In this work we address the problem of forest species recognition which is a very challenging task and has several potential applications in the wood industry. The first contribution of this work is a database composed of 22 different species of the Brazilian flora that has been carefully labeled by expert in wood anatomy. In addition, in this work we demonstrate through a series of comprehensive experiments that color-based features are quite useful to increase the discrimination power for this kind of application. Last but not least, we propose a segmentation approach so that a wood can be locally processed to mitigate the intra-class variability featured in some classes. Such an approach also brings important contribution to improve the final performance in terms of classification.

In many practical applications, new training data is acquired at different points in time, after a classification system has originally been trained. For instance, in face recognition systems, new training data may become available to enroll or to update knowledge of an individual. In this paper, a neural network classifier applied to video-based face recognition is adapted through supervised incremental learning of real-world video data. A training strategy based on particle swarm optimization is employed to co-optimize the weights, architecture and hyperparameters of the fuzzy ARTMAP network during incremental learning of new data. The performance of fuzzy ARTMAP is compared under different class update scenarios when incremental learning is performed according to 3 cases-(A) hyperparameters set to standard values, (B) hyperparameters optimized only at the beginning of the learning process with all classes, and (C) hyperparameters re-optimized whenever new training data becomes available. Overall results indicate that when samples from each individual enrolled to the system are employed for optimization, a higher classification rate is achieved and the solutions produced are more robust to variations caused by pattern presentation order. When all classes are refined equally, this is true with incremental learning according to case (C), whereas, if one class is refined at a time, best performance is obtained with case (B). However, optimizing hyperparameters requires more resources: several training sequences are needed to find the optimal solution and fuzzy ARTMAP with hyperparameters optimized according to classification rate tends to generate a high number of category nodes over longer convergence time.

Hidden Markov Models (HMMs) have been shown to provide a high level performance for detecting anomalies in intrusion detection systems. Since incomplete training data is always employed in practice, and environments being monitored are susceptible to changes, a system for anomaly detection should update its HMM parameters in response to new training data from the environment. Several techniques have been proposed in literature for on-line learning of HMM parameters. However, the theoretical convergence of these algorithms is based on an infinite stream of data for optimal performances. When learning sequences with a finite length, on-line incremental versions of these algorithms can improve discrimination by allowing for convergence over several training iterations. In this paper, the performance of these techniques is compared for learning new sequences of training data in host-based intrusion detection. The discrimination of HMMs trained with different techniques is assessed from data corresponding to sequences of system calls to the operating system kernel. In addition, the resource requirements are assessed through an analysis of time and memory complexity. Results suggest that the techniques for online incremental learning of HMM parameters can provide a higher level of discrimination than those for on-line learning, yet require significantly fewer resources than with batch training. On-line incremental learning techniques may provide a promising solution for adaptive intrusion detection systems.

Support Vector Machines (SVM) are very powerful classifiers in theory but their efficiency in practice rely on an optimal selection of hyper-parameters. A naïve or ad hoc choice of values for the latter can lead to poor performance in terms of generalization error and high complexity of parameterized models obtained in terms of the number of support vectors identified. This hyper-parameter estimation with respect to the aforementioned performance measures is often called the model selection problem in the SVM research community. In this paper we propose a strategy to select optimal SVM models in a dynamic fashion in order to attend that when knowledge about the environment is updated with new observations and previously parameterized models need to be re-evaluated, and in some cases discarded in favour of revised models. This strategy combines the power of the swarm intelligence theory with the conventional grid-search method in order to progressively identify and sort out potential solutions using dynamically updated training datasets. Experimental results demonstrate that the proposed method outperforms the traditional approaches tested against it while saving considerable computational time.

In this work we propose a methodology for document reconstruction based on dynamic programming and a modified version of the Prim's algorithm. Firstly, we use polygonal approximation to reduce the complexity of the boundaries and extract features from them. Thereafter, these features are used to feed the LCS dynamic programming algorithm. The scores yielded by the LCS algorithm are then used into a modified Prim's algorithm to find the best match among all pieces. Comprehensive experiments on a database composed of 100 shredded documents support the efficiency of the proposed methodology. When compared to global search algorithms, this approach brings an improvement of 18% in the number of fragments reconstructed.

In host-based intrusion detection systems (HIDS), anomaly detection involves monitoring for significant deviations from normal system behavior. Hidden Markov Models (HMMs) have been shown to provide a high level performance for detecting anomalies in sequences of system calls to the operating system kernel. Although the number of hidden states is a critical parameter for HMM performance, it is often chosen heuristically or empirically, by selecting the single value that provides the best performance on training data. However, this single best HMM does not typically provide a high level of performance over the entire detection space. This paper presents a multiple-HMMs approach, where each HMM is trained using a different number of hidden states, and where HMM responses are combined in the receiver operating characteristics (ROC) space according to the maximum realizable ROC (MRROC) technique. The performance of this approach is compared favorably to that of a single best HMM and to a traditional sequence matching technique called STIDE, using different synthetic HIDS data sets. Results indicate that this approach provides a higher level of performance over a wide range of training set sizes with various alphabet sizes and irregularity indices, and different anomaly sizes, without a significant computational and storage overhead.

In this paper, an approach based on the combination of discrete Hidden Markov Models (HMMs) in the ROC space is proposed to improve the performance of off-line signature verification (SV) systems designed from limited and unbalanced training data. This approach is inspired by the multiple-hypothesis principle, and allows the system to choose, from a set of different HMMs, the most suitable solution for a given input sample. By training an ensemble of user-specific HMMs with different number of states, and then combining these models in the ROC space, it is pos-sible to construct a composite ROC curve that provides a more accurate estimation of system’s performance during training and significantly reduces the error rates during op-erations. The experiments performed by using a real-world SV database with random, simple and skilled forgeries, in-dicated that the proposed approach can reduce the average error rates by more than 17%. 1

Different strategies for combination of complementary features in an HMM-based method for handwritten character recognition are evaluated. In addition, a noise reduction method is proposed to deal with the negative impact of low probability symbols in the training database. New sequences of observations are generated based on the original ones, but considering a noise reduction process. The experimental results based on 52 classes of alphabetic characters and more than 23,000 samples have shown that the strategies proposed to optimize the HMM-based recognition method are very promising.

An effective way to improve a classification method's performance is to create ensembles of classifiers. Two elements are believed to be important in constructing an ensemble: (a) the performance of each individual classifier; and (b) diversity among the classifiers. Nevertheless, most works based on diversity suggest that there exists only weak correlation between classifier performance and ensemble accuracy. We propose compound diversity functions which combine the diversities with the performance of each individual classifier, and show that there is a strong correlation between the proposed functions and ensemble accuracy. Calculation of the correlations with different ensemble creation methods, different problems and different classification algorithms on 0.624 million ensembles suggests that most compound diversity functions are better than traditional diversity measures. The population-based Genetic Algorithm was used to search for the best ensembles on a handwritten numerals recognition problem and to evaluate 42.24 million ensembles. The statistical results indicate that compound diversity functions perform better than traditional diversity measures, and are helpful in selecting the best ensembles.

The optimization of many engineering systems is challenged by the solution over-fit to the data set used to evaluate potential solutions during the evolutionary process. The solution over-fit phenomenon is hard to detect and is especially prevalent in problems involving example-based training, such as pattern feature selection and pattern classifier design. For these applications, uncontrolled over-fit can lead to biased features being extracted and degraded classifier generalization abilities. This paper details the performance of a solution over-fit control strategy used in the multiobjective evolutionary optimization of a multileveled classification system. This control, embedded within a solution validation procedure, minimizes the over-fit effects without modifying the dominance relation used in the processing of candidate solutions. Extensive experimental analysis using multiobjective genetic and memetic algorithms demonstrates both the need and the efficiency of the proposed over-fit control for pattern classification systems optimization.

Hidden Markov Models (HMMs) have been shown to be useful in handwritten pattern recognition. However, owing to their fundamental structure, they have little resistance to unexpected noise among observation sequences. In other words, unexpected noise in a sequence might "break" the normal transmission of states for this sequence, making it unrecognizable to trained models. To resolve this problem, we propose a leave-one-out-training strategy, which will make the models more robust. We also propose a leave-one-out-testing method, which will compensate for some of the negative effects of this noise. The latter is actually an example of a system with a single classifier and multiple classifications. Compared with the 98.00 percent accuracy of the benchmark HMMs, the new system achieves a 98.88 percent accuracy rate on handwritten digits.

Classifier ensemble selection may be formulated as a learning task since the search algorithm operates by minimizing/maximizing the objective function. As a consequence, the selection process may be prone to overfitting. The objectives of this paper are: (1) to show how overfitting can be detected when the selection is performed by two classical search algorithms: Genetic Algorithm and Particle Swarm Optimization; and (2) to verify which algorithm is more prone to overfitting. The experimental results demonstrate that GA appears to be more affected by overfitting.

The overproduce-and-choose strategy involves the generation of an initial large pool of candidate classifiers and it is intended to test different candidate ensembles in order to select the best performing solution. The ensemble's error rate, ensemble size and diversity measures are the most frequent search criteria employed to guide this selection. By applying the error rate, we may accomplish the main objective in Pattern Recognition and Machine Learning, which is to find high-performance predictors. In terms of ensemble size, the hope is to increase the recognition rate while minimizing the number of classifiers in order to meet both the performance and low ensemble size requirements. Finally, ensembles can be more accurate than individual classifiers only when classifier members present diversity among themselves. In this paper we apply two Pareto front spread quality measures to analyze the relationship between the three main search criteria used in the overproduce-and-choose strategy. Experimental results conducted demonstrate that the combination of ensemble size and diversity does not produce conflicting multi-objective optimization problems. Moreover, we cannot decrease the generalization error rate by combining this pair of search criteria. However, when the error rate is combined with diversity or the ensemble size, we found that these measures are conflicting objective functions and that the performances of the solutions are much higher.

One of disadvantages of Hidden Markov Models (HMMs) is its low resistance to unexpected noises among observation sequences. Unexpected noises in a sequence usually “break” a sequence of observations, and then makes this sequence unrecognizable for trained models. We propose a new HMM training and testing scheme, which compensates some of the negative effects of such noises. We carried out experiment on handwritten digit recognition problem and the result suggests our proposal can be as effective as multiclassifier systems.

Ensemble of Classifiers (EoC) has been shown effective in improving the performance of single classifiers by combining their outputs. By using diverse data subsets to train classifiers, the ensemble creation methods can create diverse classifiers for the EoC. In this work, we propose a scheme to measure the data diversity directly from random subspaces and we explore the possibility of using the data diversity directly to select the best data subsets for the construction of the EoC. The applicability is tested on NIST SD19 handwritten numerals.

One of the main requirements of biometric systems is the ability of producing very low false acceptation rate, which very often can be achieved only by combining different biometric traits. The literature has shown that the pattern classification approach usually surpasses the classifier combination approach for this task. In this work we take into account the pattern classification approach, but considering the one-class classification approach. We show that one-class classification could be considered as an alternative for biometric fusion specially when the data is highly unbalanced or data from a single class is available. The results for one-class classification reported in this paper compares to the standard two-class SVM and surpasses all the conventional classifier combination rules tested.

Automatic pattern classifiers that allow for incremental learning can adapt internal class models efficiently in response to new information, without having to retrain from the start using all the cumulative training data. In this paper, the performance of two such classifiers - the fuzzy ARTMAP and Gaussian ARTMAP neural networks - are characterize and compared for supervised incremental learning in environments where class distributions are fixed. Their potential for incremental learning of new blocks of training data, after previously been trained, is assessed in terms of generalization error and resource requirements, for several synthetic pattern recognition problems. The advantages and drawbacks of these architectures are discussed for incremental learning with different data block sizes and data set structures. Overall results indicate that Gaussian ARTMAP is the more suitable for incremental learning as it usually provides an error rate that is comparable to that of batch learning for the data sets, and for a wide range of training block sizes. The better performance is a result of the representation of categories as Gaussian distributions, and of using category-specific learning rate that decreases during the training process. With all the data sets, the error rate obtained by training through incremental learning is usually significantly higher than through batch learning for fuzzy ARTMAP. Training fuzzy ARTMAP and Gaussian ARTMAP through incremental learning often requires fewer training epochs to converge, and leads to more compact networks.

In this work we address two important issues of off-line signature verification. The first one regards feature extraction. We introduce a new graphometric feature set that considers the curvature of the main strokes of the signature. The idea is to simulate the shape of the signature by using Bezier curves and then extract features from these curves. The second important aspect is the use of an ensemble of classifiers based on graphometric features to improve the reliability of the classification, hence reducing the false acceptance. The ensemble was built using a standard genetic algorithm and different fitness functions were assessed to drive the search. Thorough experiments were conduct on a database composed of 100 writers and the results compare favorably.

The main goal of this paper is to investigate the relationship between two theories widely applied to explain the success of classifiers fusion: diversity measures and margin theory. In order to achieve this, we realized an empirical study which evaluates some classical measures related to these two theories with respect to ensembles accuracy. In particular, this study revealed valuable insights on how these two theories can influence each other, and how the application of margin based measures can be useful for the evaluation and selection of ensembles of classifiers with majority voting.

Dynamic classifier selection has traditionally focused on selecting the most accurate classifier to predict the class of a particular test pattern. In this paper we propose a new dynamic selection method to select, from a population of ensembles, the most confident ensemble of classifiers to label the test sample. Such a level of confidence is measured by calculating the ambiguity of the ensemble on each test sample. We show theoretically and experimentally that choosing the ensemble of classifiers, from a population of high accurate ensembles, with lowest ambiguity among its members leads to increase the level of confidence of classification, consequently, increasing the generalization performance. Experimental results conducted to compare the proposed method to static selection and DCS-LA, demonstrate that our method outperforms both DCS-LA and static selection strategies when a population of high accurate ensembles is available.

The manual signature-based authentication of a large number of documents is a laborious and time-consuming task. Consequently many off-line signature verification sys- tems were recently developed. In this paper we propose a human-centric system, which exploits the synergy between human and machine capabilities, and show that this com- bined system can perform better (than humans or a ma- chine) for almost all operating costs. The combination stra- tegy is based on techniques in receiver operating charac- teristics (ROC) analysis. We conduct an experiment on a data set that contains 765 test signatures from 51 writers, and record the performance of 23 human classifiers, and that of a hidden Markov model-based (HMM-based) clas- sifier, in ROC space. We propose that a manager (human or machine) specifies acceptable operating costs (Neyman- Pearson criterion), after which our human-centric system makes an optimal decision by utilizing the maximum attain- able combined classifier.

For handwritten pattern recognition, multiple classifier system has been shown to be useful in improving recognition rates. One of the most important issues to optimize a multiple classifier system is to select a group of adequate classifiers, known as ensemble of classifiers (EoC), from a pool of classifiers. Static selection schemes select an EoC for all test patterns, and dynamic selection schemes select different classifiers for different test patterns. Nevertheless, it has been shown that traditional dynamic selection does not give better performance than static selection. We propose four new dynamic selection schemes which explore the property of the oracle concept. The result suggests that the proposed schemes are apparently better than the static selection using the majority voting rule for combining classifiers.

We present a methodology to analyze Multiple Classifiers Systems (MCS) performance, using the diversity concept. The goal is to define an alternative approach to the conventional recognition rate criterion, which usually requires an exhaustive combination search. This approach defines a Distance-based Disagreement (DbD) measure using an Euclidean distance computed between confusion matrices and a soft-correlation rule to indicate the most likely candidates to the best classifiers ensemble. As case study, we apply this strategy to two different handwritten recognition systems. Experimental results indicate that the method proposed can be used as a low-cost alternative to conventional approaches.

In this work we present a strategy for off-line signature verification. It takes into account a writer-independent model which reduces the pattern recognition problem to a 2-class problem, hence, makes it possible to build robust signature verification systems even when few signatures per writer are available. Receiver operating characteristic (ROC) curves are used to improve the performance of the proposed system . The contribution of this paper is two-fold. First of all, we analyze the impacts of choosing different fusion strategies to combine the partial decisions yielded by the SVM classifiers. Then ROC produced by different classifiers are combined using maximum likelihood analysis, producing an ROC combined classifier. Through comprehensive experiments on a database composed of 100 writers, we demonstrate that the ROC combined classifier based on the writer-independent approach can reduce considerably false rejection rate while keeping false acceptance rates at acceptable levels.

Classification systems optimization is often performed with population based genetic algorithms. These methods are known for their efficacy on solving these problems, but are associated to a high computational burden with classification systems. This paper evaluates an annealing based approach to optimize a classification system, and compares results obtained with a multi-objective genetic algorithm in the same problem. Experiments conducted with isolated handwritten digits demonstrate the effectiveness of the annealing based approach, which encourages further research in this direction.

Various methods for ensemble selection and classifier combination have been designed to optimize the results of ensembles of classifiers. Genetic algorithm (GA) which uses the diversity for the ensemble selection could be very time consuming. We propose compound diversity functions as objective functions for a faster and more effective GA searching. Classifiers selected by GA are combined by a proposed pairwise confusion matrix transformation, which offer strong performance boost for EoCs.

In this paper a particle swarm optimization (PSO)-based training strategy is introduced for fuzzy ARTMAP that minimizes generalization error while optimizing parameter values. Through a comprehensive set simulations, it has been shown that this training strategy allows fuzzy ARTMAP to achieve a significantly lower generalization error than when it uses typical training strategies. Furthermore, the PSO strategy eliminates degradation of generalization error due to overtraining resulting from the training set size, number of training epochs, and data set structure. Overall results obtained with the PSO strategy reveal the importance of optimizing parameters and weights using a consistent objective function. In fact, the parameters found using this strategy vary significantly according to, e.g., training set size and data set structure, and always differ considerably from the popular choice of parameters that allows to minimize resources.

An ensemble of classifiers has been shown to be effective in improving classifier performance. Two elements are believed to be viable in constructing an ensemble: a) the classification accuracy of each individual classifier; and b) diversity among the classifiers. Nevertheless, most works based on diversity suggest that there exists only weak correlation between diversity and ensemble accuracy. We propose compound diversity functions which combine the diversities with the classification accuracy of each individual classifier, and show that with Random subspaces ensemble creation method, there is a strong correlation between the proposed functions and ensemble accuracy. The statistical result indicates that compound diversity functions perform better than traditional diversity measures.

The optimization of classification systems is often confronted by the solution over-fit problem. Solution over-fit occurs when the optimized classifier memorizes the training data sets instead of producing a general model. This paper compares two validation strategies used to control the over-fit phenomenon in classifier optimization problems. Both strategies are implemented within the multi-objective NSGA-II and MOMA algorithms to optimize a projection distance classifier and a multiple layer perceptron neural network classifier, in both single and ensemble of classifier configurations. Results indicated that the use of a validation stage during the optimization process is superior to validation performed after the optimization process.

Many recent works have investigated methods to select subsets of classifiers instead of combining all available classifiers. The majority of these works has concluded that the combiner error rate is better than diversity to guide the selection process in order to identify the best performing subset of classifiers. However, the classifier selection process has to take into account three different aspects: complexity, overfitting and performance. These aspects of the selection process have not yet been tackled simultaneously in the literature. The study presented in this paper, deals with these three aspects in a handwritten digit recognition problem. Different search criteria such as diversity, error rate and number of classifiers are applied in single and multi-objective optimization approaches using genetic algorithms. In our experiments, we observed that error rate applied in a single optimization approach was the best objective function to increase performance. The generalized diversity and interrater agreement measures, combined with error rate in pairs of objective functions were the best measures to reduce complexity and keep good performance in a multi-objective optimization approach. Finally, the performance of the solutions found in both, single and multi-objective optimization processes were increased by applying a global validation method to reduce overfitting.

In this paper we describe a synthetic database composed of 273,452 handwritten touching digits pairs to assess segmentation algorithms. It contains several different kinds of touching and it was generated by connecting 2,000 images of isolated digits extracted from the NIST SD19. In order to get a better insight on the proposed database and establish some parameters for further comparisons, we carried out experiments using four state-of-the-art segmentation algorithms.

This paper addresses the issue of class related reject thresholds for cascading classifier systems. It has been demonstrated in the literature that class related reject thresholds provide an error-reject tradeoff better than a single global threshold. In this work we argue that the error-reject tradeoff yielded by class-related reject thresholds can be further improved if a proper algorithm is used to find the thresholds. In light of this, we propose using a recently developed optimization algorithm called particle swarm optimization. It has been proved to be very effective in solving real valued global optimization problems. In order to show the benefits of such an algorithm, we have applied it to optimize the thresholds of a cascading classifier system devoted to recognize handwritten digits.

This paper presents a two-level approach to create ensemble of classifiers based on intelligent feature extraction and multi-objective genetic optimization. The first stage optimizes a set of representations, which is used to create classifiers. The second stage then optimizes the ensemble's aggregated classifiers. To assess the approach's feasibility, a set of tests with isolated handwritten digits is performed. The experimental results encourage further researches in this direction, as the optimized ensemble of classifiers outperforms the single classifier approach.

In this paper, the impact of overtraining on the performance of fuzzy ARTMAP neural networks is assessed for pattern recognition problems consisting of overlapping class distributions, and consisting of complex decision boundaries with no overlap. Computer simulations are performed with fuzzy ARTMAP networks trained for one epoch, through cross-validation, and until network convergence, using several data sets representing these pattern recognition problems. By comparing the generalisation error and resources required by these networks, the extent of overtraining due to factors such as data set structure, training strategy, number of training epochs, data normalisation, and training set size, is demonstrated. A significant degradation in fuzzy ARTMAP performance due to overtraining is shown to depend on the training set size and the number of training epochs for pattern recognition problems with overlapping class distributions.

In this paper, we propose a comparison of several post-processing methods for estimating multi-class probabilities with standard support vector machines. The different approaches have been tested on a real pattern recognition problem with a large number of training samples. The best results have been obtained by using a "one against air coupling strategy along with a softmax function optimized by minimizing the negative log-likelihood of the training data. Finally, the analysis of the error-reject tradeoff have shown that SVM allows to estimate probabilities more accurate than a classical MLP, which is indeed promising in the view of incorporated within pattern recognition system using probabilistic framework.

In this paper we address the issue of class-related reject thresholds for classification systems. It has been demonstrated in the literature that class related reject thresholds provide an error-reject tradeoff better than a single global threshold. In this work we argue that the error-reject tradeoff yielded by class related reject thresholds can be further improved if a proper algorithm is used to find the thresholds. In light of this, we propose using a recently developed optimization algorithm called particle swarm optimization. It has been proved to be very effective in solving real valued global optimization problems. In order to show the benefits of such an algorithm, we have applied it to optimize the thresholds of a cascading classifier system devoted to recognize handwritten digits.

This paper presents a novel approach for the verification of the word hypotheses generated by a large vocabulary, offline handwritten word recognition system. Given a word image, the recognition system produces a ranked list of the N-best recognition hypotheses consisting of text transcripts, segmentation boundaries of the word hypotheses into characters, and recognition scores. The verification consists of an estimation of the probability of each segment representing a known class of character. Then, character probabilities are combined to produce word confidence scores which are further integrated with the recognition scores produced by the recognition system. The N-best recognition hypothesis list is reranked based on such composite scores. In the end, rejection rules are invoked to either accept the best recognition hypothesis of such a list or to reject the input word image. The use of the verification approach has improved the word recognition rate as well as the reliability of the recognition system, while not causing significant delays in the recognition process. Our approach is described in detail and the experimental results on a large database of unconstrained handwritten words extracted from postal envelopes are presented.

A hybrid MOEA is used to solve a biobjective version of the capacitated exam proximity problem. In this MOEA, the traditional genetic crossover is replaced by two local search operators. One of the search operators is designed to repair unfeasible timetables produced by the initialization procedure and the mutation operator. The other search operator implements a simplified VNS (variable neighborhood search) meta-heuristic to improve the proximity cost. The resulting nondominated timetables are compared to four other optimization methods using six enrolment datasets. The hybrid MOEA was able to produce the lowest proximity cost for two datasets and the second lowest cost for the remaining four datasets.

The study investigates the perceptual feature similarity between different lexicons based on visual perception of the words and their representation through an observation sequence. We confirm that it is possible to use databases, which are similar in terms of morphological/perceptual features to improve the recognition performance. In this work, we demonstrated through experimentation, that it is possible to improve the recognition rate of handwritten Portuguese words by adding samples of French words in the training set. Experimental results show the efficiency of this strategy reducing the error rate.

In this paper we propose a parallel approach for the K-meansVector Quantization (VQ) algorithm used in a two-stageHidden Markov Model (HMM)-based system forrecognizing handwritten numeral strings. With thisparallel algorithm, based on the master/slave paradigm,we overcome two drawbacks of the sequential version: a)the time taken to create the codebook; and b) the amountof memory necessary to work with large trainingdatabases. Distributing the training samples over theslaves' local disks reduces the overhead associated withthe communication process. In addition, modelspredicting computation and communication time havebeen developed. These models are useful to predict theoptimal number of slaves taking into account the numberof training samples and codebook size.

This paper investigates different strategies allowing integrationof contextual information during the feature extractionstage of a cursive handwriting HMM-based recognitionsystem. First we propose to use linear discriminant analysis(LDA) in order to integrate the class information duringfeature set building. Secondly several zoning strategies areused to integrate local contextual information. Finally, aweighting technique is proposed in association with zoningwith the aim of integrating handwriting style. Some experimentswere carried out and the results show the interest ofthe proposed strategies.

In this paper a word recognition and verification scheme based on HMMs is presented. However, the main contribution of the current work lies in the validation of such a strategy. In order to perform this task, we carried out some experiments on word recognition using a legal amount database and then we compared the results reached with other study which makes use of the same database. The experiments demonstrate the efficiency of the strategy we developed for word recognition and verification.

... learning is proposed. It makes use of a multiobjective genetic algorithm where the minimization of the number of features and a validity index that measures the quality of clusters have been used to guide the search towards the more discriminant features and the best number of clusters. The proposed strategy is evaluated using two synthetic data sets and then it is applied to handwritten month word recognition. Comprehensive experiments demonstrate the feasibility and efficiency of the proposed methodology.

Feature selection for ensembles has shown to be an effective strategy for ensemble creation. In this paper we present an ensemble feature selection approach based on a hierarchical multi-objective genetic algorithm. The first level performs feature selection in order to generate a set of good classifiers while the second one combines them to provide a set of powerful ensembles. The proposed method is evaluated in the context of handwritten digit recognition, using three different feature sets and neural networks (MLP) as classifiers. Experiments conducted on NIST SD19 demonstrated the effectiveness of the proposed strategy.

This paper discusses the use of multi objective evolutionary algorithms applied to the engineering of zoning for handwriten recognition. Usually a task fulfilled by an human expert, zoning design relies on specific domain knowledge and a trial and error process to select an adequate design. Our proposed approach to automatically define the zone design was tested and was able to define zoning strategies that performed better than our former strategy defined manually.

In this paper a methodology for feature selection for the handwritten digit string recognition is proposed. Its novelty lies in the use of a multiobjective genetic algorithm where sensitivity analysis and neural network are employed to allow the use of a representative database to evaluate fitness and the use of a validation database to identify the subsets of selected features that provide a good generalization. Some advantages of this approach include the ability to accommodate multiple criteria such as number of features and accuracy of the classifier, as well as the capacity to deal with huge databases in order to adequately represent the pattern recognition problem. Comprehensive experiments on the NIST SD19 demonstrate the feasibility of the proposed methodology.

This paper describes a fast two-level Viterbi search algorithm for recognizing handwritten words as a sequence of characters concatenated according to a lexicon. The algorithm is based on hidden Markov model (HMM) representations of characters and it breaks up the computation of word likelihood scores into two levels: state level and character level. This enables the reuse of likelihood scores of characters to decode all words in the lexicon, avoiding repeated computation of state sequences. Experimental results with an 85,000-word vocabulary indicate that the computational cost of an off-line handwritten word recognition system may be reduced by more than a factor of 20 while not introducing search errors.

A modular system to recognize handwritten numerical strings is proposed. It uses a segmentation-based recognition approach and a recognition and verification strategy. The approach combines the outputs from different levels such as segmentation, recognition, and postprocessing in a probabilistic model. A new verification scheme which contains two verifiers to deal with the problems of oversegmentation and undersegmentation is presented. A new feature set is also introduced to feed the oversegmentation verifier. A postprocessor based on a deterministic automaton is used and the global decision module makes an accept/reject decision. Finally, experimental results on two databases are presented: numerical amounts on Brazilian bank checks and NIST SD19. The latter aims at validating the concept of modular system and showing the robustness of the system using a well-known database.

The authors propose a handwritten numeral string recognition method composed of two HMM-based stages. The first stage uses an implicit segmentation strategy based on string contextual information to provide multiple segmentation-recognition paths. These paths are verified and re-ranked by using a verification stage based on a digit classifier. It allows the use of two sets of features and numeral models: one taking into account both segmentation and recognition aspects in an implicit segmentation based strategy, and another considering just recognition aspects of isolated digits. The two system stages are shown to be complementary in the sense that the verification stage is shown to be a promising idea to deal with the loss in terms of recognition performance brought about by the necessary tradeoff between segmentation and recognition carried out in the first system stage.

The paper presents the application of the Mutual Information criterion (T.M. Cover and J.A. Thomas, 1991) to validate feature sets extracted from handwritten words in Brazilian legal amounts. The lexicon includes a subset of short words without ascenders/descenders and subsets of words with the same prefix or suffix. These particularities of the Brazilian lexicon show that it is necessary to improve the perpetual feature set with complementary geometric features, and also modeling the prefix and suffix of the words. Finally, the experiments show the viability of our approach.

During the development of a hidden Markov model based handwriting recognition system, the testing phase takes a non-negligible amount of computation time. This is especially true for real application where the lexicon size is large. In order to shorten the development process, we propose an indicator of the system discrimination power. This indicator is calculated during training and its final value is obtained at the end of the training phase, without more calculation. Its definition consists of a modification of the observation probability of the validation corpus by the trained system. Some experiments were carried out and the results show clearly the correlation between this indicator and recognition rates.

The problem of signature verification is in theory a pattern recognition task used to discriminate two classes, original and forgery signatures. Even after many efforts in order to develop new verification techniques for static signature verification, the influence of the forgery types has not been extensively studied. This paper reports the contribution to signature verification considering different forgery types in an HMM framework. The experiments have shown that the error rates of the simple and random forgery signatures are very closed. This reflects the real applications in which the simple forgeries represent the principal fraudulent case. In addition, the experiments show promising results in skilled forgery verification by using simple static and pseudodynamic features.

Many offline handwritten word recognition systems have been proposed since the early nineties. Most systems reported high recognition rates, however, they overlooked a very important factor in the process: speed factor. The authors explore the potential for speeding up an offline handwritten word recognition system via concurrency. The goal of the system is to achieve both full accuracy and high speed when taking into account large vocabularies. This was accomplished by integrating the recognition process with multiprocessing and distributed computing concepts. Experimental results showed that the multiprocessing environment is very promising in enhancing a sequential offline handwritten word recognition system performance.

This paper describes an off-line system under development to process unconstrained handwritten dates on Brazilian bank cheques in an omni-writer context. We show here some improvements on our previous work on isolated month word recognition using hidden Markov models (HMM). After preprocessing, a word image is explicitly segmented into characters or pseudo-characters and represented by two feature sequences of equal length, which are combined using HMM. The word models are generated from the concatenation of appropriate character models. In addition to the small date database, we also make use of the legal amount database to increase the frequency of characters in the training and the validation sets. Although this study deals with a limited lexicon, the many similarities among the word classes can affect the performance of the recognition. Experiments show an increase in the average recognition rate from 84% to 91%. Finally, we present our perspectives of future work.

The paper presents a modular system to recognize numerical amounts on Brazilian bank cheques. The system uses a segmentation-based recognition approach and the recognition function is based on a recognition and verification strategy. Our approach consists of combining the outputs from different levels such as segmentation, recognition and post-processing in a probabilistic model. A new feature set is introduced to the verifier module in order to detect segmentation effects such as over-segmentation and under-segmentation. Finally, we present experimental results on two databases: numerical amounts and NIST SD19. The latter aims at validating the concept of modular system and showing the robustness of the system over a well-known database.

Two strategies can be considered in handwriting recognition: phrase or word approaches. In this paper, we demonstrate the superiority of the phrase-based strategy, especially in city name recognition. The performance of an HMM-based off-line system using an analytic approach with explicit segmentation is evaluated on two databases: (i) city names in full, and (ii) city names in single words. A difference in performance is observed, principally caused by the dissimilarity of word lengths between the two databases. After generating other data sets and lexicons, experiments were performed yielding results which lead us to conclude that word length in the data set, as well as in lexicons, significantly influences recognition performance, and also that it is preferable to perform city name recognition based on the phrase approach rather than by word recognition.

An approach to correct the baseline handwritten word skew in the image of bank check dates is presented. The main goal of such an approach is to reduce the use of empirical thresholds. The weighted least squares approach is used on the pseudo-convex hull obtained from the mathematical morphology.

Describes a hidden Markov model-based approach designed to recognize off-line unconstrained handwritten words for large vocabularies. After preprocessing, a word image is segmented into letters or pseudoletters and represented by two feature sequences of equal length, each consisting of an alternating sequence of shape-symbols and segmentation-symbols, which are both explicitly modeled. The word model is made up of the concatenation of appropriate letter models consisting of elementary HMMs and an HMM-based interpolation technique is used to optimally combine the two feature sets. Two rejection mechanisms are considered depending on whether or not the word image is guaranteed to belong to the lexicon. Experiments carried out on real-life data show that the proposed approach can be successfully used for handwritten word recognition.

Handwriting and signature are often studied without any connection, In this paper, we present a method applied both to handwriting and signature classification that is based on their fractal behavior. First is presented the method we have developed for the computation of the fractal dimension and the secondary dimension of writing. We describe how these parameters allow us to define a pertinent representation space. We also show how this approach has permitted to extract classes related to writing and signature styles. Lastly, this method has allowed us to give evidence of the independence between the behaviors of the writer when he signs and when he writes. Such an independence will be a source of very enriching information within the context of signature authentication.

Describes the latest developments to enhance the performance of our HMM-based handwritten word recognition system. These methods only deal with the recognition phase and involve the improvement of the HMM architecture as well as the optimization of the training phase. Experiments carried out on real data show that the proposed approaches lead to significant improvements in the accuracy of the system.

In this paper, we present a new family of parameters for handwriting analysis based on the fractal behavior of writing. These parameter allow the classification of writing into different families which could be used as a preliminary step for recognition methods. They also constitute a way to quantify some legibility properties, and we show that they can be useful for cognitive approaches to handwriting analysis.

The main theme of the paper is the automatic segmentation of Arabic words using mathematical morphology tools. The proposed algorithm has been tested with a set of Arabic words written by different writers, ranging from poor to acceptable quality. The initial experimental results are very encouraging and promising.

Shape matrices have been used as a representation of planar shapes like industrial parts or printed characters. We investigate the use of shape matrices as a mixed shape factor for offline signature verification. By mixed shape factor we mean any global shape factor where the position of local measurements are taken into account in the definition of a similarity measure between two representations. It is demonstrated that when using a good similarity measure between two shape matrices, this shape factor is relatively well suited for the global interpretation of signature images.

This paper presents an Off-line Signature Verification System for identifying random forgeries aimed at banking application. The cognitive information learning process in the proposed system is inspired by some characteristics of human learning. Four features distinguish the proposed system from those proposed thus far. First, the verification task is accomplished without a priori knowledge of the class of random forgeries. Second, no explicit modeling or making geometrical measurements are used to represent the signature. Third, the decision of the system is made throughout the use of two-stage verification process by which a global and/or local analysis are performed on the unknown signature. The global analysis is concerned with the overall shape of the unknown signature, whereas, the local analysis is concerned with the local features composing the unknown signature. Fourth, these analysis are performed at the boundary or within a predefined search region called the identity grid designed for each writer in the system. The proposed system is evaluated with a data base of 800 signatures.

This work investigates the use of a fuzzy ARTMAP neural network for detecting cancerous cells, based on the one-class problem approach. This approach is inspired by the way human beings perform pattern recognition. We all know that children and adults alike are capable of detecting patterns belonging to a certain class, by learning the features of these patterns only. Moreover, a child or an adult is capable of detecting an unknown pattern belonging to another class without an a priori knowledge of the features in these patterns. Based on this approach, a fuzzy ARTMAPs-based system is developed for detecting cancerous cells by training the fuzzy ARTMAPs with the features belonging to the class of cancerous cells only. This is different from the two-class problem approach which requires that the classifier must be trained with features from the class of cancerous cells and the class of noncancerous cells. Experimental analysis were conducted using a set of 542 patterns taken from a sample of breast cancer. Training was performed with 383 cancerous cells. System performance was evaluated using 54 cancerous cells and 159 noncancerous cells. Evaluation results show 98% correct identification of cancerous cells and 95% correct identification of noncancerous cells.

A fundamental problem in the field of off-line signature verification is the lack of a pertinent shape representation or shape factor. The main difficulty in the definition of pertinent features lies in the local variability of the signature line which is closely related to the intrinsic characteristic of human beings. We propose a new formalism for signature representation based on visual perception. A signature image of 512/spl times/128 pixels is centered onto a grid of rectangular retinas which are excited by a local portion of the signature image. So each retina has only a local perception of the entire scene. Granulometric size distributions have been used for the definition of local shape descriptors in attempt to characterized the amount of signal activity in front of each retina located on the focus of attention grid. Experimental evaluation of this scheme has been made using a signature database of 800 genuine signatures from 20 individuals. Two types of classifiers, a INN and a threshold classifiers show a total error rate below 0.02% and 1.0% respectively in the context of random forgeries.

A neural network approach is proposed to build the first stage of an automatic handwritten signature verification system that will eliminate random and simple forgeries rapidly. The directional probability density function was used as a global shape factor, and its discriminatory power was enhanced by reducing its cardinality. The choice of the best pretreatment was made by means of a k nearest neighbour classifier. This study has shown that the cardinality of the PDF can be reduced by a factor of ten while doubling its discriminatory power. The backpropagation model was retained to build the neural network classifier. An experimental protocol was used to find the best configuration of the BPN classifier whose performance was compared on the same database and with the same decision rule (without rejection criteria), to those of the kNN and threshold classifiers. This comparison shows that the BPN classifier is clearly better than the T classifier, and compares favourably with the kNN classifier.

This work proposes a new approach to signature verification. It is inspired by the human learning and the approach adopted by the expert examiner of signatures, in which an a priori knowledge of the class of forgeries is not required in order to perform the verification task. Based on this approach, we present a Fuzzy ARTMAP based system for the elimination of random forgeries. Compared to the conventional systems proposed thus far, the presented system is trained with genuine signatures only. Six experiments have been performed on a data base of 200 signatures taken from five writers (40 signatures/writer). Evaluation of the system was measured using different numbers of training signatures.

For pt.I see Proc. 12th ICPR, p.450-3. In a real situation, the choice of the best representation R(/spl gamma/) for the implementation of a signature verification system able to cope with all types of handwriting is a very difficult task. This study is original in that the design of the integrated classifiers E(x) is based on a large number of individual classifiers e/sub k/(x) (or signature representations R(/spl gamma/)) in an attempt to overcome in some way the need for feature selection. In this paper, the authors present a first systematical evaluation of a multi-classifier-based approach for off-line signature verification. Two types of integrated classifiers based on kNN or minimum distance classifiers and 15 types of representation related to the ESC used as a shape factor have been evaluated using a signature database of 800 images (20 writers/spl times/40 signatures per writer) in the context of random forgeries.

We present a new method to segment visual handwritten data in gray-scale images. In handwriting recognition, visual shapes are very important in improving the system's performance. We introduce a robust method for extracting visual shapes of handwritten data from a noisy background. We adopted a multi-resolution Marr-Hildreth (1980) based approach to correctly segment visual data in variable contrasted images. Encouraging results have been obtained on real data, from the CEDAR database.>

In this paper, the authors present an evaluation of the extended shadow code (ESC) used as a global feature vector for the signature verification problem. The proposed class of shape factors seems to be a good compromise between global features related to the general aspect of the signature, and local features related to measurements taken on specific parts of the signature. This is achieved by the bar mask definition, where at low resolution the ESC is related to the overall proportions of the signature. At high resolution, values of the horizontal, vertical and diagonal bars could be related to local measurements taken on specific parts of the signature without requiring low-level handwriting segmentation which is a very difficult task.

The interpretation of handwritten signature images should be closely related to the writer’s identity. The representation and analysis of the handwritten signature is the major challenge in the field of automatic signature verification. A new concept of representation and interpretation of handwritten signature images is advocated. The segmentation process breaks up the signature into a collection of arbitrarily-shaped primitives. In the next step, a local interpretation process serves as a sophisticated template matching, permitting the labeling of all primitives from the test primitive set. This is followed by the global interpretation process, which permits the evaluation of a similarity measure between two structural graphs. Experimental results obtained from a database of 800 handwritten signature images from 20 writers show a performance with a type I error rate of ∈1=1.50%, a type II error rate of ∈2=1.37% and a total error rate ∈t=1.43% in the best strategy proposed using a minimum-distance classifier and two reference signatures. A complete description of this novel automatic handwritten signature verification system is presented in this paper.

Evaluates the extended shadow code used as a global feature vector for the signature verification problem. The latter as a shape factor is very easy to implement. Numerical experiments have been made with a signature database of 800 images (20 writers /spl times/ 40 signatures per writer) in the context of random forgeries. In the first experiment, a kNN classifier with voting shows a mean total error rate of 0.01% with k=1. In the second experiment, a minimum distance classifier has been used. The thresholds were evaluated for each of the 20 writers, and the number of reference signatures was varied in the range 1 /spl les/ N/sub ref/ /spl les/ 10. The mean total error rate was below 1.00% with N/sub ref/ = 4 genuine signatures.>

The first stage of a complete automatic handwritten signature verification system (AHSVS) is described in this paper. Since only random forgeries are taken into account in this first stage of decision, the directional probability density function (PDF) which is related to the overall shape of the handwritten signature has been taken into account as feature vector. Experimental results show that using both directional PDFs and the completely connected feedforward neural network classifier are valuable to build the first stage of a complete AHSVS.>

A preliminary design is given for a general image-understanding system for the extraction of a signature representation. A novel version of an algorithm for centroidal-linkage region-growing with merging, using the statistics of directional data is given. It permits the extraction of textured regions characterized by local uniformity in the orientation of the gradient.>