Person re-identification (ReID) across aerial and ground vantage points has become crucial for large-scale surveillance and public safety applications. Although significant progress has been made in ground-only scenarios, bridging the aerial-ground domain gap remains a formidable challenge due to extreme viewpoint differences, scale variations, and occlusions. Building upon the achievements of the AG-ReID 2023 Challenge, this paper introduces the AG-VPReID 2025 Challenge—the first large-scale video-based competition focused on high-altitude (80–120 m) aerial-ground person ReID. Constructed on the new AG-VPReID dataset with 3,027 identities, over 13,500 tracklets, and approximately 3.7 million frames captured from UAVs, CCTV, and wearable cameras, the challenge featured four international teams. These teams developed solutions ranging from multi-stream architectures to transformer-based temporal reasoning and physics-informed modeling. The leading approach, X-TFCLIP from UAM, attained 72.28% Rank-1 accuracy in the aerial-to-ground ReID setting and 70.77% in the ground-to-aerial ReID setting, surpassing existing baselines while highlighting the dataset’s complexity. For additional details, please refer to the official website at https://agvpreid25.github.io.

Behavioral biometrics based on smartphone motion sensors are growing in popularity for authentication purposes. In this study, AirSignatureDB is presented: a new publicly accessible dataset of in-air signatures collected from 108 participants under real-world conditions, using 83 different smartphone models across four sessions. This dataset includes genuine samples and skilled forgeries, enabling a comprehensive evaluation of system robustness against realistic attack scenarios. Traditional and deep learningbased methods for in-air signature verification are benchmarked, while analyzing the influence of sensor modality and enrollment strategies. Beyond verification, a first approach to reconstructing the three-dimensional trajectory of in-air signatures from inertial sensor data alone is introduced. Using on-line handwritten signatures as a reference, we demonstrate that the recovery of accurate trajectories is feasible, challenging the long-held assumption that in-air gestures are inherently traceless. Although this approach enables forensic traceability, it also raises critical questions about the privacy boundaries of behavioral biometrics. Our findings underscore the need for a reevaluation of the privacy assumptions surrounding inertial sensor data, as they can reveal user-specific information that had not previously been considered in the design of in-air signature systems.

Active Membership Inference Test (aMINT) is a method designed to detect whether given data were used during the training of machine learning models. In Active MINT, we propose a novel multitask learning process that involves training simultaneously two models: the original or Audited Model, and a secondary model, referred to as the MINT Model, responsible for identifying the data used for training the Audited Model. This novel multi-task learning approach has been designed to incorporate the auditability of the model as an optimization objective during the training process of neural networks. The proposed approach incorporates intermediate activation maps as inputs to the MINT layers, which are trained to enhance the detection of training data. We present results using a wide range of neural networks, from lighter architectures such as MobileNet to more complex ones such as Vision Transformers, evaluated in 5 public benchmarks. Our proposed Active MINT achieves over 80% accuracy in detecting if given data was used for training, significantly outperforming previous approaches in the literature. Our aMINT and related methodological developments contribute to increasing transparency in AI models, facilitating stronger safeguards in AI deployments to achieve proper security, privacy, and copyright protection.

Abstract Maintaining a healthy lifestyle has become increasingly challenging in today’s sedentary society marked by poor eating habits. To address this issue, both national and international organisations have made numerous efforts to promote healthier diets and increased physical activity. However, implementing these recommendations in daily life can be difficult, as they are often generic and not tailored to individuals. This study presents the AI4Food-NutritionDB database, the first nutrition database that incorporates food images and a nutrition taxonomy based on recommendations by national and international health authorities. The database offers a multi-level categorisation, comprising 6 nutritional levels, 19 main categories (e.g., “Meat”), 73 subcategories (e.g., “White Meat”), and 893 specific food products (e.g., “Chicken”). The AI4Food-NutritionDB opens the doors to new food computing approaches in terms of food intake frequency, quality, and categorisation. Also, we present a standardised experimental protocol and benchmark including three tasks based on the nutrition taxonomy (i.e., category, subcategory, and final product recognition). These resources are available to the research community, including our deep learning models trained on AI4Food-NutritionDB, which can serve as pre-trained models, achieving accurate recognition results for challenging food image databases. All these resources are available in GitHub ( https://github.com/BiDAlab/AI4Food-NutritionDB ).

This article presents the Keystroke Verification Challenge - onGoing (KVC-onGoing) 1 1 https://sites.google.com/view/bida-kvc/ . , on which researchers can easily benchmark their systems in a common platform using large-scale public databases, the Aalto University Keystroke databases, and a standard experimental protocol. The keystroke data consist of tweet-long sequences of variable transcript text from over 185,000 subjects, acquired through desktop and mobile keyboards simulating real-life conditions. The results on the evaluation set of KVC-onGoing have proved the high discriminative power of keystroke dynamics, reaching values as low as 3.33% of Equal Error Rate (EER) and 11.96% of False Non-Match Rate (FNMR) @1% False Match Rate (FMR) in the desktop scenario, and 3.61% of EER and 17.44% of FNMR @1% at FMR in the mobile scenario, significantly improving previous state-of-the-art results. Concerning demographic fairness, the analyzed scores reflect the subjects’ age and gender to various extents, not negligible in a few cases. The framework runs on CodaLab 2 2 https://codalab.lisn.upsaclay.fr/competitions/14063 . . • We set up a novel framework for developing and evaluating keystroke biometrics. • We designed a unified experimental protocol with desktop and mobile scenarios. • We employ the biggest databases of keystroke dynamics, with over 185,000 subjects. • We provide a competitive performance baseline based on a limited-time challenge. • We provide a first exploration of the biometric fairness of keystroke dynamics.

Abstract This article proposes a novel children–computer interaction (CCI) approach for the task of age group detection. This approach focuses on the automatic analysis of the time series generated from the interaction of the children with mobile devices. In particular, we extract a set of 25 time series related to spatial, pressure, and kinematic information of the children interaction while colouring a tree through a pen stylus tablet, a specific test from the large-scale public ChildCIdb database. A complete analysis of the proposed approach is carried out using different time series selection techniques to choose the most discriminative ones for the age group detection task: (i) a statistical analysis and (ii) an automatic algorithm called sequential forward search (SFS). In addition, different classification algorithms such as dynamic time warping barycenter averaging (DBA) and hidden Markov models (HMM) are studied. Accuracy results over 85% are achieved, outperforming previous approaches in the literature and in more challenging age group conditions. Finally, the approach presented in this study can benefit many children-related applications, for example, towards an age-appropriate environment with the technology.

Abstract International Organizations urge the protection of our oceans and their ecosystems due to their immeasurable importance to humankind. Since illegal fishing activities, commonly known as IUU fishing, cause irreparable damage to these ecosystems, concerned organisms are pushing to detect and combat IUU fishing practices. The automatic identification system allows to locate the position and trajectory of fishing vessels. In this study we address the task of detecting vessels’ fishing gears based on the trajectory behavior defined by GPS position data, a useful task to prevent the proliferation of IUU fishing practices. We present a new database including trajectories that span 7 different fishing gears and analyze these as in a time sequence analysis problem. We leverage from feature extraction techniques from the online signature verification domain to model vessel trajectories, and extract relevant information in the form of both local and global feature sets. We show how, based on these sets of features, the kinematics of vessels according to different fishing gears can be effectively classified using common supervised learning algorithms with accuracies up to $$90\%$$ 90 % . Furthermore, motivated by the concerns raised by several organizations on the adverse impact of bottom trawling on marine biodiversity, we present a binary classification experiment in which we were able to distinguish this kind of fishing gear with an accuracy of $$99\%$$ 99 % . We also illustrate in an ablation study the relevance of factors such as data availability and the sampling period to perform fishing gear classification. Compared to existing works, we highlight these factors, especially the importance of using sampling periods in the order of minutes instead of hours.

We present edBB-Demo, a demonstrator of an AI-powered research platform for student monitoring in remote education. The edBB platform aims to study the challenges associated to user recognition and behavior understanding in digital platforms. This platform has been developed for data collection, acquiring signals from a variety of sensors including keyboard, mouse, webcam, microphone, smartwatch, and an Electroencephalography band. The information captured from the sensors during the student sessions is modelled in a multimodal learning framework. The demonstrator includes: i) Biometric user authentication in an unsupervised environment; ii) Human action recognition based on remote video analysis; iii) Heart rate estimation from webcam video; and iv) Attention level estimation from facial expression analysis.

Institute, Warsaw, Poland This paper describes the results of the IEEE BigData 2023 Keystroke Verification Challenge1(KVC), that considers the biometric verification performance of Keystroke Dynamics (KD), captured as tweet-long sequences of variable transcript text from over 185,000 subjects. The data are obtained from two of the largest public databases of KD up to date, the Aalto Desktop and Mobile Keystroke Databases, guaranteeing a minimum amount of data per subject, age and gender annotations, absence of corrupted data, and avoiding excessively unbalanced subject distributions with respect to the considered demographic attributes. Several neural architectures were proposed by the participants, leading to global Equal Error Rates (EERs) as low as 3.33% and 3.61% achieved by the best team respectively in the desktop and mobile scenario, outperforming the current state of the art biometric verification performance for KD. Hosted on CodaLab2, the KVC will be made ongoing to represent a useful tool for the research community to compare different approaches under the same experimental conditions and to deepen the knowledge of the field.

The new regulatory framework proposal on Artificial Intelligence (AI) published by the European Commission establishes a new risk-based legal approach. The proposal highlights the need to develop adequate risk assessments for the different uses of AI. This risk assessment should address, among others, the detection and mitigation of bias in AI. In this work we analyze statistical approaches to measure biases in automatic decision-making systems. We focus our experiments in face recognition technologies. We propose a novel way to measure the biases in machine learning models using a statistical approach based on the N-Sigma method. N-Sigma is a popular statistical approach used to validate hypotheses in general science such as physics and social areas and its application to machine learning is yet unexplored. In this work we study how to apply this methodology to develop new risk assessment frameworks based on bias analysis and we discuss the main advantages and drawbacks with respect to other popular statistical tests.

This paper describes the experimental framework and results of the IJCB 2022 Mobile Behavioral Biometrics Competition (MobileB2C). The aim of MobileB2C is bench-marking mobile user authentication systems based on behavioral biometric traits transparently acquired by mobile devices during ordinary Human-Computer Interaction (HCI), using a novel public database, BehavePassDB11https://github.com/BiDAlab/MobileB2C_BehavePassDE, and a standard experimental protocol. The competition is divided into four tasks corresponding to typical user activities: keystroke, text reading, gallery swiping, and tapping. The data are composed of touchscreen data and several background sensor data simultaneously acquired. “Random” (different users with different devices) and “skilled” (different user on the same device attempting to imitate the legitimate one) impostor scenarios are considered. The results achieved by the participants show the feasibility of user authentication through behavioral biometrics, although this proves to be a non-trivial challenge. MobileB2C will be established as an on-going competition22https://sites.google.com/view/mobileb2c/.

This article presents SVC-onGoing1, an on-going competition for on-line signature verification where researchers can easily benchmark their systems against the state of the art in an open common platform using large-scale public databases, such as DeepSignDB2 and SVC2021_EvalDB3, and standard experimental protocols. SVC-onGoing is based on the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021), which has been extended to allow participants anytime. The goal of SVC-onGoing is to evaluate the limits of on-line signature verification systems on popular scenarios (office/mobile) and writing inputs (stylus/finger) through large-scale public databases. Three different tasks are considered in the competition, simulating realistic scenarios as both random and skilled forgeries are simultaneously considered on each task. The results obtained in SVC-onGoing prove the high potential of deep learning methods in comparison with traditional methods. In particular, the best signature verification system has obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3). Future studies in the field should be oriented to improve the performance of signature verification systems on the challenging mobile scenarios of SVC-onGoing in which several mobile devices and the finger are used during the signature acquisition.

This work proposes and analyzes the use of keystroke biometrics for content de-anonymization. Fake news have become a powerful tool to manipulate public opinion, especially during major events. In particular, the massive spread of fake news during the COVID-19 pandemic has forced governments and companies to fight against missinformation. In this context, the ability to link multiple accounts or profiles that spread such malicious content on the Internet while hiding in anonymity would enable proactive identification and blacklisting. Behavioral biometrics can be powerful tools in this fight. In this work, we have analyzed how the latest advances in keystroke biometric recognition can help to link behavioral typing patterns in experiments involving 100,000 users and more than 1 million typed sequences. Our proposed system is based on Recurrent Neural Networks adapted to the context of content de-anonymization. Assuming the challenge to link the typed content of a target user in a pool of candidate profiles, our results show that keystroke recognition can be used to reduce the list of candidate profiles by more than 90%. In addition, when keystroke is combined with auxiliary data (such as location), our system achieves a Rank-1 identification performance equal to 52.6% and 10.9% for a background candidate list composed of 1K and 100K profiles, respectively.

The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, presentation attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. In this context, we present a novel fingerprint presentation attack detection (PAD) scheme based on i) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and ii) an in-depth analysis of several state-of-theart techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.35% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks).

Passwords are still used on a daily basis for all kind of applications. However, they are not secure enough by themselves in many cases. This work enhances password scenarios through two-factor authentication asking the users to draw each character of the password instead of typing them as usual. The main contributions of this study are as follows: i) We present the novel MobileTouchDB public database, acquired in an unsupervised mobile scenario with no restrictions in terms of position, posture, and devices. This database contains more than 64K on-line character samples performed by 217 users, with 94 different smartphone models, and up to 6 acquisition sessions. ii) We perform a complete analysis of the proposed approach considering both traditional authentication systems such as Dynamic Time Warping (DTW) and novel approaches based on Recurrent Neural Networks (RNNs). In addition, we present a novel approach named Time-Aligned Recurrent Neural Networks (TA-RNNs). This approach combines the potential of DTW and RNNs to train more robust systems against attacks. A complete analysis of the proposed approach is carried out using both MobileTouchDB and e-BioDigitDB databases. Our proposed TA-RNN system outperforms the state of the art, achieving a final 2.38% Equal Error Rate, using just a 4-digit password and one training sample per character. These results encourage the deployment of our proposed approach in comparison with traditional typed-based password systems where the attack would have 100% success rate under the same impostor scenario.

In this paper we evaluate the suitability of handwriting patterns as potential biomarkers to model Parkinson's disease (PD). Although the study of PD is attracting the interest of many researchers around the world, databases to evaluate handwriting patterns are scarce and knowledge about patterns associated to PD is limited and biased to the existing datasets. This paper introduces a database with a total of 935 handwriting tasks collected from 55 PD patients and 94 healthy controls (45 young and 49 old). Three feature sets are extracted from the signals: neuromotor, kinematic, and nonlinear dynamic. Different classifiers are used to discriminate between PD and healthy subjects: support vector machines, k-nearest neighbors, and a multilayer perceptron. The proposed features and classifiers enable to detect PD with accuracies between 81% and 97%. Additionally, new insights are presented on the utility of the studied features for monitoring and detecting PD.

Data have become one of the most valuable things in this new era where deep learning technology seems to overcome traditional approaches. However, in some tasks, such as the verification of handwritten signatures, the amount of publicly available data is scarce, what makes difficult to test the real limits of deep learning. In addition to the lack of public data, it is not easy to evaluate the improvements of novel approaches compared with the state of the art as different experimental protocols and conditions are usually considered for different signature databases. To tackle all these mentioned problems, the main contribution of this study is twofold: i) we present and describe the new DeepSignDB on-line handwritten signature biometric public database, and ii) we propose a standard experimental protocol and benchmark to be used for the research community in order to perform a fair comparison of novel approaches with the state of the art. The DeepSignDB database is obtained through the combination of some of the most popular on-line signature databases, and a novel dataset not presented yet. It comprises more than 70K signatures acquired using both stylus and finger inputs from a total of 1526 users. Two acquisition scenarios are considered, office and mobile, with a total of 8 different devices. Additionally, different types of impostors and number of acquisition sessions are considered along the database. The DeepSignDB and benchmark results are available in GitHub.

This paper proposes a novel approach for on-line signature complexity detection based on Recurrent Neural Networks (RNNs). Complexity of handwritten signatures can vary from very simple ones (just a simple flourish) to very complex signatures (including the handwritten full name and complex flourish). Three different complexity levels are proposed: low, medium, and high. Time functions are extracted from the on-line signatures and a system based on RNNs (BLSTM in particular) is trained to classify the three levels of complexity over a ground truth manually labelled database (BiosecurID with 400 subjects). This initial model is used to automatically label a very large database (DeepSignDB) containing over 1500 subjects, which is then used to train the proposed RNN for signature complexity detection. Promising results ca. 85% of accuracy are achieved. This complexity detector could be used as a first stage in a signature verification system in order to train a specific biometric system per signature complexity level and improve the overall system performance.

Architectures based on Recurrent Neural Networks (RNNs) have been successfully applied to many different tasks such as speech or handwriting recognition with state-of-the art results. The main contribution of this work is to analyse the feasibility of RNNs for on-line signature verification in real practical scenarios. We have considered a system based on Long Short-Term Memory (LSTM) with a Siamese architecture whose goal is to learn a similarity metric from pairs of signatures. For the experimental work, the BiosecurID database comprised of 400 users and 4 separated acquisition sessions are considered. Our proposed LSTM RNN system has outperformed the results of recent published works on the BiosecurID benchmark in figures ranging from 17.76% to 28.00% relative verification performance improvement for skilled forgeries.

On-line signature verification systems are mainly based on two approaches: feature- or time functions-based systems (a.k.a. global and local systems). However, new sources of information can be also considered in order to complement these traditional approaches, reduce the intra-class variability and achieve more robust signature verification systems against forgers. In this paper we focus on the use of the concept of complexity in on-line signature verification systems. The main contributions of the present work are: 1) classification of users according to the complexity level of their signatures using features extracted from the Sigma LogNormal writing generation model, and 2) a new architecture for signature verification exploiting signature complexity that results in highly improved performance. Our proposed approach is tested considering the BiosecurID on-line signature database with a total of 400 users. Results of 5.8% FRR for a FAR = 5.0% have been achieved against skilled forgeries outperforming recent related works. In addition, an analysis of the optimal time functions for each complexity level is performed providing practical insights for the application of signature verification in real scenarios.

The use of Millimetre wave images has been proposed recently in the biometric field to overcome certain limitations when using images acquired at visible frequencies. In this paper, several body shape-based techniques were applied to model the silhouette of images of people acquired at 94 GHz. We put forward several methods for the parameterization and classification stage with the objective of finding the best configuration in terms of biometric recognition performance. Contour coordinates, shape contexts, Fourier descriptors and silhouette landmarks were used as feature approaches and for classification we utilized Euclidean distance and a dynamic programming method. Results showed that the dynamic programming algorithm improved the performance of the system with respect to the baseline Euclidean distance and the necessity of a minimum resolution of the contour to achieve promising equal error rates. The use of the contour coordinates is the most suitable feature to use in the system regarding the performance and the computational cost involved when having at least 3 images for model training. Besides, Fourier descriptors are more robust against rotations, which may be of interest when dealing with few training images.

Comparing a latent fingerprint minutiae set against a ten print fingerprint minutiae set using an automated fingerprint identification system is a challenging problem. This is mainly because latent fingerprints obtained from crime scenes are mostly partial fingerprints, and most automated systems expect approximately the same number of minutiae between query and the reference fingerprint under comparison for good performance. In this work, we propose a methodology to reduce the minutiae set of ten print with respect to that of query latent minutiae set by registering the orientation field of latent fingerprint with the ten print orientation field. By reducing the search space of minutiae from the ten print, we can improve the performance of automated identification systems for latent fingerprints. We report the performance of our registration algorithm on the NIST-SD27 database as well as the improvement in the Rank Identification accuracy of a standard minutiae-based automated system.

Footstep recognition is a relatively new biometric which aims to discriminate people using walking characteristics extracted from floor-based sensors. This paper reports for the first time a comparative assessment of the spatiotemporal information contained in the footstep signals for person recognition. Experiments are carried out on the largest footstep database collected to date, with almost 20,000 valid footstep signals and more than 120 people. Results show very similar performance for both spatial and temporal approaches (5 to 15 percent EER depending on the experimental setup), and a significant improvement is achieved for their fusion (2.5 to 10 percent EER). The assessment protocol is focused on the influence of the quantity of data used in the reference models, which serves to simulate conditions of different potential applications such as smart homes or security access scenarios.

Questioned document examination is extensively used by forensic specialists for criminal identification. This paper presents a writer recognition system based on allographic features operating in identification mode (one-to-many). It works at the level of isolated characters, considering that each writer uses a reduced number of shapes for each one. Individual characters of a writer are manually segmented and labeled by an expert as pertaining to one of 62 alphanumeric classes (10 numbers and 52 letters, including lowercase and uppercase letters), being the particular setup used by the forensic laboratory participating in this work. A codebook of shapes is then generated by clustering and the probability distribution function of allograph usage is the discriminative feature used for recognition. Results obtained on a database of 30 writers from real forensic documents show that the character class information given by the manual analysis provides a valuable source of improvement, justifying the proposed approach. We also evaluate the selection of different alphanumeric channels, showing a dependence between the size of the hit list and the number of channels needed for optimal performance.

The kinematical information present in synthetically generated signatures is analyzed using the Sigma-Lognormal model and compared to the kinematical properties of real samples. Experiments are carried out on totally independent development and test sets and show a high degree of similarity between humanly produced and artificial signatures. One particular flaw is found in the velocity profile of synthetic signatures. Two possible solutions are proposed to improve the synthetic generation method using the Kinematic Theory of rapid human movements.

Touch screen-enabled devices are proliferating in the communications and entertainment markets. In this scenario, the use of graphical passwords for user validation is receiving an increasing interest in the last years. Unlike in other fields of research on automatic user authentication, such as biometrics, there are no public databases of graphical passwords usable for research purposes (to the extent of our knowledge). In the present work, the recently captured DooDB database is introduced. This database comprises two sub corpora: doodles and simplified signatures (pseudo-signatures). These data were produced by 100 users, who were asked to draw with their fingertips over a mobile device touch screen. Forgeries are also included in the database. A quantitative analysis of both datasets is first performed. Preliminary verification experiments using the two kinds of graphical passwords are reported.

This work studies the performance of a state-of-the-art fingerprint recognition technology, in several practical scenarios of interest in forensic casework. First, the differences in performance between manual and automatic minutiae extraction for latent fingerprints are presented. Then, automatic minutiae extraction is analyzed using three different types of fingerprints: latent, rolled and plain. The experiments are carried out using a database of latent finger marks and fingerprint impressions from real forensic cases. The results show high performance degradation in automatic minutiae extraction compared to manual extraction by human experts. Moreover, high degradation in performance on latent finger marks can be observed in comparison to fingerprint impressions.

A new multimodal biometric database designed and acquired within the framework of the European BioSecure Network of Excellence is presented. It is comprised of more than 600 individuals acquired simultaneously in three scenarios: 1) over the Internet, 2) in an office environment with desktop PC, and 3) in indoor/outdoor environments with mobile portable hardware. The three scenarios include a common part of audio/video data. Also, signature and fingerprint data have been acquired both with desktop PC and mobile portable hardware. Additionally, hand and iris data were acquired in the second scenario using desktop PC. Acquisition has been conducted by 11 European institutions. Additional features of the BioSecure Multimodal Database (BMDB) are: two acquisition sessions, several sensors in certain modalities, balanced gender and age distributions, multimodal realistic scenarios with simple and quick tasks per modality, cross-European diversity, availability of demographic data, and compatibility with other multimodal databases. The novel acquisition conditions of the BMDB allow us to perform new challenging research and evaluation of either monomodal or multimodal biometric systems, as in the recent BioSecure Multimodal Evaluation campaign. A description of this campaign including baseline results of individual modalities from the new database is also given. The database is expected to be available for research purposes through the BioSecure Association during 2008.

Recent years have seen the rapid spread of biometric technologies for automatic people recognition. However, security and privacy issues still represent the main obstacles for the deployment of biometric-based authentication systems. In this paper, we propose an approach, which we refer to as BioConvolving, that is able to guarantee security and renewability to biometric templates. Specifically, we introduce a set of noninvertible transformations, which can be applied to any biometrics whose template can be represented by a set of sequences, in order to generate multiple transformed versions of the template. Once the transformation is performed, retrieving the original data from the transformed template is computationally as hard as random guessing. As a proof of concept, the proposed approach is applied to an on-line signature recognition system, where a hidden Markov model-based matching strategy is employed. The performance of a protected on-line signature recognition system employing the proposed BioConvolving approach is evaluated, both in terms of authentication rates and renewability capacity, using the MCYT signature database. The reported extensive set of experiments shows that protected and renewable biometric templates can be properly generated and used for recognition, at the expense of a slight degradation in authentication performance.

In this paper, we study the impact of an incremental level of skill in the forgeries against signature verification systems. Experiments are carried out using both off-line systems, involving the discrimination of signatures written on a piece of paper, and on-line systems, in which dynamic information of the signing process (such as velocity and acceleration) is also available. We use for our experiments the BiosecurID database, which contains both on-line and off-line versions of signatures, acquired in four sessions across a 4 month time span with incremental level of skill in the forgeries for different sessions. We compare several scenarios with different size and variability of the enrolment set, showing that the problem of skilled forgeries can be alleviated as we consider more signatures for enrolment.

A brute force attack using synthetically generated handwritten signatures is performed against a HMM-based signature recognition system. The generation algorithm of synthetic signatures is based on the spectral analysis of the trajectory functions and has proven to produce very realistic results. The experiments are carried out by attacking real signature models from the MCYT database (which comprises 8,250 signature samples from 330 users). Results show that such an attack is feasible, thus arising the necessity of introducing countermeasures against this type of vulnerability in real applications.

A novel scheme to generate multiple synthetic samples from a real on-line handwritten signature is proposed. The algorithm models a transmission channel which introduces a certain distortion into the real signature to produce the different synthetic samples. The method is used to increase the amount of data of the clients enrolling on a state-of-the-art HMM-based signature verification system. The enhanced enrollment results in performance improve up to70% between the case in which only one real sample of the user was available for the training, and the case where the proposed algorithm was used to generate additional synthetic training data.

This paper evaluates the combination of static image (off-line) and dynamic information (on-line) for signature verification. Two off-line and two on-line recognition approaches exploiting information at the global and local levels are used. Experimental results are given using the BiosecurID database (130 signers, 3,640 signatures). Fusion experiments are done using a trained fusion approach based on linear logistic regression. It is shown experimentally that the local systems outperform the global ones, both in the on-line and in the off-line case. We also observe a considerable improvement when combining the two on-line systems, which is not the case with the off-line systems. The best performance is obtained when fusing all the systems together, which is specially evident for skilled forgeries when enough training data is available.

Automatically verifying the identity of a person by means of biometrics (e.g., face and fingerprint) is an important application in our day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject in the literature, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw biometric images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both the template and the query data. The response to the call of the evaluation campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this campaign is the first attempt to benchmark quality-based multimodal fusion algorithms. In the presence of changing image quality which may be due to a change of acquisition devices and/or device capturing configurations, we observe that the top performing fusion algorithms are those that exploit automatically derived quality measurements. Our evaluation also suggests that while using all the available biometric sensors can definitely increase the fusion performance, this comes at the expense of increased cost in terms of acquisition time, computation time, the physical cost of hardware, and its maintenance cost. As demonstrated in our experiments, a promising solution which minimizes the composite cost is sequential fusion, where a fusion algorithm sequentially uses match scores until a desired confidence is reached, or until all the match scores are exhausted, before outputting the final combined score.

A biometric template protection system for dynamic signature verification is presented. The approach uses auxiliary (helper) data that allows the matching with secure templates but do not provide information to a potential attacker. The performance of the proposed system is evaluated using the MCYT signature database comprising 330 users, with 25 genuine signatures and 25 skilled forgeries per user. The results show similar performance compared to the baseline unprotected system. However, the security of the proposed system against attacks to the template database is significantly higher.

A performance and robustness study for on-line signature verification is presented. Experiments are carried out on the MCYT database comprising 16,500 signatures from 330 subjects, which are parameterized by means of a 100-feature set which can be divided into four different groups according to the signature information they contain, namely: (i) time, (ii) speed and acceleration, (iii) direction, and (iv) geometry. The SFFS feature selection algorithm is used to search for the best performing feature subsets under the skilled and random forgeries scenarios, and to find the most robust subsets against a hill-climbing attack. Comparative experiments are given, where it is shown that the most discriminant parameters are those regarding geometry information, while the most robust are the time related features.

The proliferation of handheld devices such as PDAs and smart phones represents a new scenario for automatic signature verification. Traditionally, research on signature verification has been carried out employing signatures acquired using digitizing tablets or Tablet-PCs. In this paper we study the effects of the mobile acquisition conditions and we analyze the considerations that must be taken in the new handheld scenario. A signature verification system adapted to handheld devices via feature selection is proposed and a systematic comparison with a traditional pen tablet-based system is performed. The system is combined with another based on hidden Markov models using score fusion. Results confirm an increased signature variability in the case of handheld devices.

Signal-quality awareness has been found to increase recognition rates and to support decisions in multisensor environments significantly. Nevertheless, automatic quality assessment is still an open issue. Here, we study the orientation tensor of fingerprint images to quantify signal impairments, such as noise, lack of structure, blur, with the help of symmetry descriptors. A strongly reduced reference is especially favorable in biometrics, but less information is not sufficient for the approach. This is also supported by numerous experiments involving a simpler quality estimator, a trained method (NFIQ), as well as the human perception of fingerprint quality on several public databases. Furthermore, quality measurements are extensively reused to adapt fusion parameters in a monomodal multialgorithm fingerprint recognition environment. In this study, several trained and nontrained score-level fusion schemes are investigated. A Bayes-based strategy for incorporating experts' past performances and current quality conditions, a novel cascaded scheme for computational efficiency, besides simple fusion rules, is presented. The quantitative results favor quality awareness under all aspects, boosting recognition rates and fusing differently skilled experts efficiently as well as effectively (by training).

In the present contribution, the applicability of off-line handwritten signatures to the fuzzy vault construction is studied. Feature extraction is based on quantized maxima and minima from upper and lower envelopes of the signature. Baseline results are reported for skilled and random forgeries of the MCYT off-line signature database, showing that the proposed scheme is suitable for signers with good separability between genuine signatures and skilled forgeries.

In this work, resampling techniques and interpolation are applied to on-line signatures. Their effect on the performance of an on-line signature system using hidden Markov models is studied. The presented techniques are based on linear interpolation and Catmull-Rom cubic splines. Experimental results are provided on the MCYT database comprising 16,500 signatures from 330 subjects and as many skilled forgeries. Our approach allows to reduce the sampling rate of the on-line signature capture system, leading to the reduction of storage resource requirements and increased simplicity without compromising and even enhancing the system performance. This also leads to the discussion of which is the minimum sampling rate for HMM-based dynamic signature verification.

Performance in terms of accuracy is one of the most important goal of a biometric system. Hence, having a measure which is able to predict the performance with respect to a particular sample of interest is specially useful, and can be exploited in a number of ways. In this paper, we present two automatic measures for predicting the performance in off-line signature verification. Results obtained on a sub-corpus of the MCYT signature database confirms a relationship between the proposed measures and system error rates measured in terms of equal error rate (EER), false acceptance rate (FAR) and false rejection rate (FRR).

Forensic DNA profiling is acknowledged as the model for a scientifically defensible approach in forensic identification science, as it meets the most stringent court admissibility requirements demanding transparency in scientific evaluation of evidence and testability of systems and protocols. In this paper, we propose a unified approach to forensic speaker recognition (FSR) oriented to fulfil these admissibility requirements within a framework which is transparent, testable, and understandable, both for scientists and fact-finders. We show how the evaluation of DNA evidence, which is based on a probabilistic similarity-typicality metric in the form of likelihood ratios (LR), can also be generalized to continuous LR estimation, thus providing a common framework for phonetic-linguistic methods and automatic systems. We highlight the importance of calibration, and we exemplify with LRs from diphthongal F-pattern, and LRs in NIST-SRE06 tasks. The application of the proposed approach in daily casework remains a sensitive issue, and special caution is enjoined. Our objective is to show how traditional and automatic FSR methodologies can be transparent and testable, but simultaneously remain conscious of the present limitations. We conclude with a discussion on the combined use of traditional and automatic approaches and current challenges for the admissibility of speech evidence.

One of the open issues in fingerprint verification is the lack of robustness against image-quality degradation. Poor-quality images result in spurious and missing features, thus degrading the performance of the overall system. Therefore, it is important for a fingerprint recognition system to estimate the quality and validity of the captured fingerprint images. In this work, we review existing approaches for fingerprint image-quality estimation, including the rationale behind the published measures and visual examples showing their behavior under different quality conditions. We have also tested a selection of fingerprint image-quality estimation algorithms. For the experiments, we employ the BioSec multimodal baseline corpus, which includes 19 200 fingerprint images from 200 individuals acquired in two sessions with three different sensors. The behavior of the selected quality measures is compared, showing high correlation between them in most cases. The effect of low-quality samples in the verification performance is also studied for a widely available minutiae-based fingerprint matching system.

Information fusion in fingerprint recognition has been studied in several papers. However, only a few papers have been focused on sensor interoperability and sensor fusion. In this paper, these two topics are studied using a multisensor database acquired with three different fingerprint sensors. Authentication experiments using minutiae and ridge-based matchers are reported. Results show that the performance drops dramatically when matching images from different sensors. We have also observed that fusing scores from different sensors results in better performance than fusing different instances from the same sensor

Score normalization methods in biometric verification, which encompass the more traditional user-dependent decision thresholding techniques, are reviewed from a test hypotheses point of view. These are classified into test dependent and target dependent methods. The focus of the paper is on target dependent score normalization techniques, which are further classified into impostor-centric, target-centric, and target-impostor methods. These are applied to an on-line signature verification system on signature data from the First International Signature Verification Competition (SVC 2004). In particular, a target-centric technique based on the cross-validation procedure provides the best relative performance improvement testing both with skilled (19%) and random forgeries (53%) as compared to the raw verification performance without score normalization (7.14% and 1.06% Equal Error Rate for skilled and random forgeries, respectively).

A novel strategy for combining general and user-dependent knowledge in a multimodal biometric verification system is presented. It is based on SVM classifiers and trade-off coefficients introduced in the standard SVM training problem. Experiments are reported on a bimodal biometric system based on fingerprint and on-line signature traits. A comparison between three fusion strategies, namely user-independent, user-dependent and the proposed adapted user-dependent, is carried out. As a result, the suggested approach outperforms the former ones. In particular, a highly remarkable relative improvement of 68% in the EER with respect to the user-independent approach is achieved. The severe and very common problem of training data scarcity in the user-dependent strategy is also relaxed by the proposed scheme, resulting in a relative improvement of 40% in the EER compared to the raw user-dependent strategy.

This paper proposes a support vector machine (SVM) based combining scheme that incorporates ideolectal and acoustic characteristics for speaker recognition. Two statistical model paradigms, namely GMM for acoustic modeling and bigrams for language modeling, provide multilevel speaker information that affords a better classification performance when SVM-based fusion is accomplished. This combining approach is useful for all speaker recognition tasks where a considerable amount of data is available. Motivated by the absence of Spanish databases that made feasible our research experiments, more than nine hours of Spanish conversational speech was collected and manually transcribed from broadcasted radio talk shows.

We show how any speaker recognition system can be adapted to provide its results according to the Bayesian approach for evidence analysis and forensic reporting. This approach, firmly established in other forensic areas as fingerprint, DNA or fiber analysis, suits the needs of both the court and the forensic scientist. We show the inadequacy of the classical approach to forensic reporting because of the use of thresholds and the suppression of the prior probabilities related to the case. We also show how to assess the performance of those forensic systems through Tippet plots. Finally, an example is shown using NIST-Ahumada eval'2001 data, where the speaker recognition abilities of our system are assessed through DET plots, using then these raw scores as evidences into the forensic system, where relative to populations we will obtain the corresponding likelihood ratios values, which are assessed through Tippet (1968) plots.

In this paper, a real time nested microphone array based on the auditory properties of the human ear is presented. Three different stages in the development of the system are described. Firstly, the design of the new auditory-based microphone array is presented, obtaining better noise reduction using the masking properties of the human auditory system. Secondly, we show its validation through a new method called E-RASTI based in the well-known RASTI (Rapid STI - speech transmission index) intelligibility estimator. In addition to classical enhancement estimators as SNR, NMR (noise to masked ratio) or AI (articulation index), E-RASTI is proposed and validated for dereverberation assessment, used here with real speech signals and not with speech-like signals as in the original RASTI method. And finally as third stage, the real time implementation of this highly computing-demanding algorithm through the use of a DSP-based architecture based on the recent floating point TMS320C6701 is described.

This paper is focused on algorithmic issues for biometric face verification (i.e., given an image of the face and an identity claim, decide whether they correspond to each other or not). Several alternatives for geometric normalization of images, photometric normalization, dimensionality reduction and similarity measures are proposed and compared using the XM2VTS database and the associated Lausanne protocol [K. Messer et al., 1999], [J. Luettin et al., 1998]. Experiments under this particular framework show that best verification results are obtained when holistic approaches for face recognition (such as eigenfaces or fisherfaces) are combined with techniques traditionally associated to local feature-based approaches, such as Gabor decompositions.

In this paper we evaluate authentication performance of the minutiae-based fingerprint automatic recognition system, previously proposed D. Simon-Zorita, et al. (2001) and recently completed, with the new large fingerprint image database, MCYT J. Ortega-Garcia, et al. (2002). The scheme includes: image enhancement, characteristic extraction and pattern recognition. The design of this database permits to analyse the influence of the variability factors appearing in the image acquisition phase. We focus in two factors: the finger position over the acquisition sensor, and the quality of the acquired fingerprint images. The analysis is accomplished in cases of supervised and nonsupervised databases. Score normalization is presented as an effective technique to improve the fingerprint verification system performance, yielding highly competitive EERs.

The aim of this paper, regarding multimodal biometric verification, is twofold: on one hand, to compare experimentally a selection of them using as monomodal baseline systems as our template-based face, minutiae-based fingerprint and HMM-based on-line signature verification systems on the MCYT multimodal database. A new strategy is proposed and discussed in order to compute a multimodal combined score by means of support vector machine (SVM) classifiers.

This paper proposed a support vector machine (SVM) based combining scheme that incorporates idiolectal and acoustic characteristics for speaker recognition. Two statistical model paradigms, namely GMM for acoustic modeling and bigrams for language modeling, provide multilevel speaker information that affords a better classification performance when SVM-based fusion is accomplished. This combining approach is useful for all speaker recognition tasks where a considerable amount of data is available. Motivated by the absence of Spanish databases that made feasible our research experiments, more than nine hours of Spanish conversational speech was collected and manually transcribed from broadcasted radio talk shows.

A complete minutiae extraction scheme for automatic fingerprint recognition systems is presented. The proposed method uses improving alternatives for the image enhancement process, leading consequently to an increase in the reliability in the minutiae extraction task. In the first stages, image normalization and the orientation field of the fingerprint are calculated. The local orientation of the ridges serve as parameter for the next processing stages. Details of the adaptive morphological filtering used for ridge extraction and background noise elimination are described. Evaluation results are obtained from both inked and scanned fingerprints. Conclusions in terms of Goodness Index (GI), which compares the results obtained by automatic minutiae extraction with manually extracted ones, are provided in order to test the global performance of this approach.

In this contribution we have addressed the problem of speech enhancement in noisy and reverberant rooms through the use of a new approach that combines the dereverberation abilities of a structure based in the separate processing of the minimum-phase and all-pass components of the input speech signals, and the noise rejection performance of a speech-activity-based Wiener filter able to cope both with coherent and diffuse noise. Experiments have been performed with the CMU real multichannel database, which includes a clean speech reference through a head-mounted microphone. This reference signal have been also used to perform simulation experiments in controlled conditions. Extensive results have been obtained, both with log area ratio and cepstral distances of input and processed signals to the reference, and with segSNR improvements, assessing the abilities of the new system to cope both with reverberation and coherent and diffuse noise in different acoustic environments.

This paper proposes a distinction between existing multilingual synthesis systems and mixed-lingual or polyglot synthesis systems. The latter should be capable of synthesising with the same voice utterances which contain foreign language words or word groups. As a first step towards polyglot synthetic speech, the design and realisation of a 4-lingual single-speaker diphone inventory is detailed. The first results show that mixedlingual sentences can be synthesised using this inventory. Further work will focus on multilingual text analysis and prosodic modelling in order to create a complete polyglot TTS system.

Speaker recognition is a major task when security applications through speech input are needed. Regarding speaker identity, several factors of variability must be considered: (a) factors concerning peculiar intra-speaker variability (manner of speaking, inter-session variability, dialectal variations, emotional condition, etc.) or forced intra-speaker variability (Lombard effect, cocktail-party effect), and (b) factors depending on external influences (kind of microphone, channel effects, noise, reverberation, etc). To cope with all these variability sources, a specific speech database called AHUMADA has been designed and collected for speaker recognition tasks in Castilian Spanish. AHUMADA incorporates six different recording sessions, including both in situ and telephone speech recordings. A total of 104 male speakers uttered isolated digits, digit strings, phonologically balanced short utterances, phonologically and syllabically balanced read text and more than one minute of spontaneous speech, so about 15 GB of speech material is available. Speaker verification results, concerning the available variability sources are also presented.

In this paper, the acoustic characteristics of sound fields in enclosed rooms are studied in the joint presence of speech and noise, in order to design a broadband microphone array system capable of coping with both coherent and diffuse noises. Several state-of-the-art speech enhancement array structures are presented and compared to our new system in terms of correct word recognition rates in a simple command and control task. The proposed structure, based on a broadband subband-nested array, performs real-time estimations of the spatial coherence in order to determine the coherent/diffuse nature of the different subbands, using different filters in each case, improving also the classical Wiener post-filter, typically used for diffuse noise supression, for proper cancellation of coherent noises. The results obtained with a 15-channel simultaneous recording database in different reverberation and noise conditions show better performance than other structures previously proposed.

Regarding speaker identity in forensic conditions, several factors of variability must be taken into account, as peculiar intra-speaker variability, forced intra-speaker variability or channel-dependent external influences. Using ‘AHUMADA’ large speech database in Spanish, containing several recording sessions and channels, and including different tasks for 100 male speakers, automatic speaker verification experiments are accomplished. Due to the inherent non-cooperative nature of speakers in forensic applications, only text-independent recognizers are likely to be used. In this sense, a GMM-based verification system has been used in order to obtain quantitative results. Maximum likelihood estimation of the models is performed, and LPC-cepstra, deltaand delta-delta-LPCC, are used at the parameterization stage. With this baseline verification system, we intend to determine how some variability sources included in ‘AHUMADA’ affect speaker identification. Results including speaking rate influence, singleand multi-session training and cross-channel testing are presented when likelihood-domain normalization is applied.

The development of a robust speaker recognition system obtained through the joint use of acoustic array processing and spectral normalization as input to a Gaussian mixture model speaker recognition system is described. Results obtained with these techniques have been reported previously by the authors, but operational problems appear if extensive testing with different configurations and testing conditions are intended. We describe an open system that has been developed to cope with this problem. The number and geometry of the microphones, the time delay estimation method, the array processing structure and the spectral normalization technique together with the room size, noise type and SNR are some of the options that can be easily changed. It will also allow testing with real multichannel databases and any new algorithm can easily be incorporated to the system.

Acoustical mismatch between training and testing phases induces degradation of performance in automatic speaker recognition systems. Providing robustness to speaker recognizers has to be, therefore, a priority matter. Robustness in the acoustical stage can be accomplished through speech enhancement techniques as a prior stage to the recognizer. These techniques are oriented to the reduction of the impact that acoustical noise produces on the input signal. In this paper, several spectral subtraction-derived techniques are used to enhance single-channel noisy speech. Other perspectives, based in dual-channel (adaptive filtering) and multi-channel (microphone arrays) processing are also presented as optimal solutions to speech enhancement needs. A comparative analysis of the proposed techniques, with different types of noise at different SNRs, as a pre-processing stage to an ergodic HMM-based speaker recognizer, is presented.

In this paper we describe the additive robustness obtained through the combined use of a first acoustic processing step based on a low complexity microphone array, followed by a spectral normalization step.Microphone arrays have shown to provide good results in reducing different sources of acoustic degradation.However, microphone arrays produce linear filtering effects that need to be compensated in order to obtain a minimal spectral distortion.In this contribution we will present the combination of a microphone array together with different well known spectral normalization techniques as preprocessing stages to a Gaussian Mixture Models (GMM) based text-independent speaker recognition system.We will show that the combination of these extensively used techniques in the fields of speech enhancement and robust speaker recognition respectively, greatly improves the results obtained when the system is tested in noisy reverberant environments with short utterances from unconstrained conversational speech.

Real world conditions differ from ideal or laboratory conditions, causing mismatch between training and testing phases, and consequently, inducing performance degradation in automatic speaker recognition systems [1].Many strategies have been adopted to cope with acoustical degradation; in some applications of speaker identification systems a clean sample of speech, prior to the recognition stage, is needed.This has justified the use of procedures that may reduce the impact of acoustical noise on the desired signal, giving rise to techniques involved in the enhancement of noisy speech [2,9].In this paper, a comparative performance analysis of singlechannel (based in classical spectral subtraction and some derived alternatives), dual-channel (based in adaptive noise cancelling) and multi-channel (using microphone arrays) speech enhancement techniques, with different types of noise at different SNRs, as a pre-processing stage to an ergodic HMMbased speaker recognizer, is presented.

The authors describe the main topics related to a real-time implementation of the Proposed Federal Standard 1016 (PFS-1016) 4800 bps CELP (code excited linear prediction) voice coder together with some alternatives to extend real-time CELP schemes over a wide range of bit rates (8000, 6500, and 2400 bps are considered). Several procedures are evaluated in order to select the options in PFS-1016 that provide the highest quality for an implementation based on a single AT&T DSP32C chip. From the basic CELP scheme, higher and lower bit rates are derived after evaluation of the efficiency obtained for the different elements in this speech coding algorithm.>