The purpose of this paper is to provide a biometric based technology roadmap to advance personalized Augmentative and Alternative Communication (AAC) systems for individuals with disabilities. The proposed technology roadmap introduces two core components: an AAC biometric register and interoperable technological modules. The biometric register provides a structured framework for capturing and transforming physiological and behavioral traits to enable adaptive and context-aware communication. The interoperable module design supports reconfigurable AAC architectures, ensuring compatibility across diverse interfaces and devices. Together, these mechanisms establish a foundation for automated and scalable personalization in AAC technologies. The study demonstrates that the proposed methodology for technology roadmapping effectively connects established research with emerging computational practices. This is confirmed by the results of the case study such as sign language recognition. • Personalized Augmentative and Alternative Communication (AAC) is a branch of assistive technologies for people with communication disabilities. Technology roadmapping that adheres to the best biometric practices is a step toward future AAC technologies. • A biometric register is a framework for AAC. It links biometric traits, such as gestures, to intermediate traits, such as synthesized speech, for customizable communication channels. AAC provides accessibility through open protocols and modular design. The methodology for AAC technology roadmapping includes the developed expert elicitation protocol.

The number of processing channels is a critical requirement for wearable stress analyzers, especially for first responder needs (firefighters, police, rescuers, paramedics, and emergency medics). This study proposes reducing the number of processing channels in a human stress analyzer, which is a multi-channel wrist wearable wireless device, by introducing a composite approach that fuses cardiovascular biomarkers into a single processing stream. The proposed composite technique is justified experimentally for stress related tasks: accuracy rates ranging from 78% to 86.2% for detecting cognitive workload and 85.2% to 90.7% for detecting the psychophysical workload.

The goal of this paper is to develop a self-aware computational model aimed at analyzing student satisfaction in an engineering faculty. We examine whether student diversity, as well as student engagement, social events, and academic experience have a direct effect on student satisfaction and, consequently, retention and loyalty. A Confirmatory Factor Analysis to explore the associations between latent and observed variables led to the design of the Structural Equation Model. In our model, the following latent variables were positively associated with Student Satisfaction: Student Engagement, Academic Experience, Student Diversity, and Social Events. Also, the association between Student Satisfaction and Student Loyalty was positive. Other latent variables were tested such as Student Well-being and Student Support, but they did not provide a good result. Note that, unlike other papers in the area, we considered student diversity to make the model diversity-aware. The learned model provided the basis for the reasoning process behind engineering student satisfaction. In future work, this Structural Equation Model will become the basis for building the Bayesian network, the model that allows us to perform probabilistic inference and test various scenarios using the reasoning mechanism.

Mass migration, caused by events such as climate change or global conflicts significantly impacts every aspect of society including social infrastructures, public service delivery, education, healthcare, and security to list a few which in turn can affect the employment market. This paper presents an innovative methodology to illustrate the causal relationship between migration and employment rates in Canada. We propose a combination of training recurrent neural networks and temporal convolutional networks for employment trend forecasting and populating Bayesian networks to reveal interrelationships across various causal networks. Applying our method shows how changes in demographics such as sex, age, education, and the country of origin of displaced individuals influence the employment rate of the host country.

Macrolevel facial muscle variations, as used for building models of seven discrete facial expressions, suffice when distinguishing between macrolevel human affective states but won’t discretise continuous and dynamic microlevel variations in facial expressions. We present a hierarchical separation and classification network (HSCN) for discovering dynamic, continuous, and macro- and microlevel variations in facial expressions of affective states. In the HSCN, we first invoke an unsupervised cosine similarity-based separation method on continuous facial expression data to extract twenty-one dynamic facial expression classes from the seven common discrete affective states. The between-clusters separation is then optimized for discovering the macrolevel changes resulting from facial muscle activations. A following step in the HSCN separates the upper and lower facial regions for realizing changes pertaining to upper and lower facial muscle activations. Data from the two separated facial regions are then clustered in a linear discriminant space using similarities in muscular activation patterns. Next, the actual dynamic expression data are mapped onto discriminant features for developing a rule-based expert system that facilitates classifying twenty-one upper and twenty-one lower microexpressions. Invoking the random forest algorithm would classify twenty-one macrolevel facial expressions with 76.11% accuracy. A support vector machine (SVM), used separately on upper and lower facial regions in tandem, could classify them with respective accuracies of 73.63% and 87.68%. This work demonstrates a novel and effective method of dynamic assessment of affective states. The HSCN further demonstrates that facial muscle variations gathered from either upper, lower, or full-face would suffice classifying affective states. We also provide new insight into discovery of microlevel facial muscle variations and their utilization in dynamic assessment of facial expressions of affective states.

Recent advancements in machine learning have enabled the use of long-term accelerometry data collection and machine learning algorithms to quickly and accurately detect upper limb weakness. Although accelerometry-derived measurements are commonly used in long-term rehabilitation studies, this study aimed to determine whether similar techniques could be used to detect short-term changes in upper limb motor function in patients who were hospitalized soon after experiencing a stroke. Six binary classification models were created by training on variable data window times of paretic upper limb accelerometer feature data, and four preliminary visualizations were proposed to provide health professionals with information on the duration, intensity, symmetry, and variability of upper limb activity. The models were evaluated using Area Under the Curve (AUC) scores to classify the data into two classes: severe or moderately severe motor function. The AUC scores ranged from 0.72 to 0.94, with higher scores indicating better model performance. While this study provides a preliminary assessment of the efficacy of using accelerometry and machine learning to characterize upper limb motor function immediately following a stroke, the results suggest that further investigation is warranted.

This study applies and validates causal graphical models for the task of assessing the risk of a stroke given the stroke patient data. A probabilistic causal (Bayesian) network is designed to evaluate the risk factors identified by a stroke expert. Structural Equation Modeling is applied on empirical data to provide a quantitative assessment of causal relationships among the variables in the Bayesian Network, thus statistically validating the expert's knowledge. Several scenarios of risk assessment using the inference mechanism on the Bayesian network are demonstrated.

The goal of this paper is to audit the applications and trends of the state-of-the-art Computational Intelligence (CI) as an assistive technology for Equity, Diversity, and Inclusion (EDI). To accomplish the audit objective, a roadmapping mechanism was developed and used for the analysis of the CI approaches that were applied and show promise for the research and development of the EDI-aware systems. The audit identifies the strategic CI reserve, comprised of machine reasoning techniques such as probabilistic causal graph models, Granger causal model, and advanced statistical approaches.

This study investigates the dynamic discretization approach with the purpose of improving probabilistic causal models for the task of toxicity risk assessment in breast radiother-apy. We considered a probabilistic causal model such as Bayesian Networks, and implemented a modified version of Fenton & Neil's dynamic discretization algorithm to validate and analyze these models in terms of performance. Our implementation performs discretization at the data- or distribution-level. This approach is shown to provide significant improvement over static methods when assessing distribution fit via relative entropy error, as well as being very computationally efficient. Predictive performance was compared across four distinct datasets using Bayesian Networks, and dynamic discretization was not found to consistently outperform static techniques despite generating discretizations with significantly better fit to their underlying distributions.

In this paper, we investigate hand gesture classifiers that rely upon the abstracted 'skeletal' data recorded using the RGB-Depth sensor. We focus on 'skeletal' data represented by the body joint coordinates, from the Praxis dataset. The PRAXIS dataset contains recordings of patients with cortical pathologies such as Alzheimer's disease, performing a Praxis test under the direction of a clinician. In this paper, we propose hand gesture classifiers that are more effective with the PRAXIS dataset than previously proposed models. Body joint data offers a compressed form of data that can be analyzed specifically for hand gesture recognition. Using a combination of windowing techniques with deep learning architecture such as a Recurrent Neural Network (RNN), we achieved an overall accuracy of 70.8% using only body joint data. In addition, we investigated a long-short-term-memory (LSTM) to extract and analyze the movement of the joints through time to recognize the hand gestures being performed and achieved a gesture recognition rate of 74.3% and 67.3% for static and dynamic gestures, respectively. The proposed approach contributed to the task of developing an automated, accurate, and inexpensive approach to diagnosing cortical pathologies for multiple healthcare applications.

Face normalization can be a crucial step when handling generic face recognition. We propose the Pose and Illumination Normalization (PIN) framework with contrast data learning for face normalization. The PIN framework is designed to learn the transformation from a source set to a target set. The source set and the target set compose a contrastive data set for learning. The source set contains faces collected in the wild and thus covers a wide range of variation across illumination, pose, expression and other variables. The target set contains face images taken under controlled conditions and all faces are in frontal pose and balanced in illumination. The PIN framework is composed of an encoder, a decoder and two discriminators. The encoder is made of a state-of-the-art face recognition network and acts as a facial feature extractor, which is not updated during training. The decoder is trained on both the source and target sets, and aims to learn the transformation from the source set to the target set; and therefore, it can transform an arbitrary face into a illumination and pose normalized face. The discriminators are trained to ensure the photo-realistic quality of the normalized face images generated by the decoder. The loss functions employed in the decoder and discriminators are appropriately designed and weighted for yielding better normalization outcomes and recognition performance. We verify the performance of the propose framework on several benchmark databases, and compare with state-of-the-art approaches.

Detecting visual relationships between people and clothing in an image has been a relatively unexplored problem in the field of computer vision and biometrics. The lack of readily available public dataset for "worn" and "unworn" classification has slowed the development of solutions for this problem. We present the release of the Relatable Clothing Dataset which contains 35287 person-clothing pairs and segmentation masks for the development of "worn" and "unworn" classification models. Additionally, we propose a novel soft attention unit for performing "worn" and "unworn" classification using deep neural networks. The proposed soft attention models have an accuracy of upward 98.55% ± 0.35% on the Relatable Clothing Dataset and demonstrate high generalizable, allowing us to classify unseen articles of clothing such as high visibility vests as "worn" or "unworn".

The focus of this paper is dynamic gesture recognition in the context of the interaction between humans and machines. We propose a model consisting of two sub-networks, a transformer and an ordered-neuron long-short-term-memory (ON-LSTM) based recurrent neural network (RNN). Each sub-network is trained to perform the task of gesture recognition using only skeleton joints. Since each sub-network extracts different types of features due to the difference in architecture, the knowledge can be shared between the sub-networks. Through knowledge distillation, the features and predictions from each sub-network are fused together into a new fusion classifier. In addition, a cyclical learning rate can be used to generate a series of models that are combined in an ensemble, in order to yield a more generalizable prediction. The proposed ensemble of knowledge-sharing models exhibits an overall accuracy of 86.11% using only skeleton information, as tested using the Dynamic Hand Gesture-14/28 dataset.

Synthetic, or artificial data is used in security applications such as protection of sensitive information, prediction of rare events, and training neural networks. Risk and trust are assessed specifically for a given kind of synthetic data and particular application. In this paper, we consider a more complicated scenario, - biometric-enabled cognitive cognitive biometric-enabled identity management, in which multiple kinds of synthetic data are used in addition to authentic data. For example, authentic biometric traits can be used to train the intelligent tools to identify humans, while synthetic, algorithmically generated data can be used to expand the training set or to model extreme situations. This paper is dedicated to understanding the potential impact of synthetic data on the cognitive checkpoint performance, and risk and trust prediction.

The development of decision support systems is a growing domain that can be applied in the area of disease control and diagnostics. Using video-based surveillance data, skeleton features are extracted to perform action recognition, specifically the detection and recognition of coughing and sneezing motions. Providing evidence of flu-like symptoms, a decision support system based on causal networks is capable of providing the operator with vital information for decision-making. A modified residual temporal convolutional network is proposed for action recognition using skeleton features. This paper addresses the capability of using results from a machine-learning model as evidence for a cognitive decision support system. We propose risk and trust measures as a metric to bridge between machine-learning and machine-reasoning. We provide experiments on evaluating the performance of the proposed network and how these performance measures can be combined with risk to generate trust.

This paper addresses the evaluation of the performance of face and facial expression biometrics through a decision support system. The evaluation criteria include capturing the risk of the system, estimating the reliability of decision, and predicting the change in the perceived operator's trust in the decision. The relevant applications include human behavior monitoring and stress detection in individuals and teams, and in situational awareness system. Using an available database of cross-spectral videos of faces and facial expressions, we conducted a series of experiments to 1) demonstrate the phenomenon of biases in biometrics that affect the evaluated measures of the performance in human-machine systems, 2) explore the overall risk of the system caused by error rates such as false match and false nonmatch rates, and 3) calculate the reliability of cross-spectral and emotion-varying face identification.

This paper addresses the challenge of detecting the breathing cessation in sleeping subjects, via breathing pattern monitoring at a distance and under "night-light" conditions. We investigate a near-infrared video-based approach to estimate the breathing rate, based on chest or back movements. A body pose estimation algorithm and the Lucas-Kanade optical flow method are combined to automatically detect the Region of Interest (ROI) represented by a grid of points. The movement of the ROI is then translated into the frequency of respiratory events. We used a dataset with 28 near-infrared videos, as well as 11 videos of subject uncovered and partially covered by blankets. We compared the breathing rate measurements provided by a wearable device with the ones estimated by the video-based approach. A linear correlation analysis of both measurements resulted in a coefficient of determination of 0.925, and accuracy of 99.70% for the first dataset, and 0.873 and 88.95% for the second dataset, respectively. The ultimate application is to detect abnormalities in breathing and health emergencies in environments such as homeless shelters.

This paper focuses on the instance segmentation of soft attributes on humans such as clothing and personal protective equipment at a hazardous workplace. We propose the use of soft biometric object classes from the Open Images V5 and DeepFashion2 datasets to pre-train a mask segmentation network to detect and segment personal protective equipment in the workplace. Preliminary results of our proposed model achieves a mean average precision, mAP50, of 61.7% with minimal optimization, resulting in very good segmentation of construction helmets, high visibility vests, welding masks, and ear protection in the workplace. Applications of the results from this paper include improving workplace safety in hazardous industries by providing a tool to ensure proper personal protective equipment usage while maintaining worker anonymity.

It is recognized that system trustworthiness is a hyperstructure embodied by the structural, behavioral, and system dimensions with a set of coherent attributes. We explore a theoretical framework of tripartite trustworthiness that can be applied to real-world autonomous systems. We present a formal study of the essences and mathematical models of system trustworthiness and their quantitative measurements in the contexts of autonomous and mission-critical intelligent systems where humans and machines interact in a hybrid environment.

Face attribute analysis from live video is a valuable aide in biometric-based person identification. This is a challenging task due to variations in lighting, occlusion, pose and other variables. To address it, we propose an effective and robust approach: extract the face features using certain selected layers of the pre-trained Convolutional Neural Network (CNN) models such as AlexNet, GoogleNet and ResNet50. We focus on the intermediate CNN layers, since the reported experimental results suggest that the best results may not always be obtained when extracting deep features using the fully connected layers. Next, we train a linear SVM on the extracted features to perform the attribute classification. We also apply a feature level fusion by concatenating the features extracted from the intermediate layers of the aforementioned networks. Our approach applied on live video achieves an average accuracy of 89.40% using the fused features which is better than the results (between 86.6% and 87%) reported for the CNNs applied only on static images.

This paper addresses the problem of biometric identification of animals, specifically dogs. We apply advanced machine learning models such as deep neural network on the photographs of pets in order to determine the pet identity. In this paper, we explore the possibility of using different types of "soft" biometrics, such as breed, height, or gender, in fusion with "hard" biometrics such as photographs of the pet's face. We apply the principle of transfer learning on different Convolutional Neural Networks, in order to create a network designed specifically for breed classification. The proposed network is able to achieve an accuracy of 90.80% and 91.29% when differentiating between the two dog breeds, for two different datasets. Without the use of "soft" biometrics, the identification rate of dogs is 78.09% but by using a decision network to incorporate "soft" biometrics, the identification rate can achieve an accuracy of 84.94%.

A major obstacle in ultra-wideband radar based approaches for object detection concealed on human body is the difficulty in collecting high quality radar signal data. Generative adversarial networks (GAN) have shown promise in synthesizing data for image and audio processing. This paper proposes the design of a GAN for application in radar signal generation. Data collected using the Finite-Difference Time-Domain (FDTD) method on three concealed object classes (no object, large object, and small object) are used as training data. A GAN is trained to generate radar signal samples for each class. The proposed GAN is capable of synthesizing the radar signal data which is indistinguishable from the training data by qualitative analysis performed by human observers.

Human activity and gesture recognition is an important component of rapidly growing domain of ambient intelligence, in particular in assisting living and smart homes. In this paper, we propose to combine the power of two deep learning techniques, the convolutional neural networks (CNN) and the recurrent neural networks (RNN), for automated hand gesture recognition using both depth and skeleton data. Each of these types of data can be used separately to train neural networks to recognize hand gestures. While RNN were reported previously to perform well in recognition of sequences of movement for each skeleton joint given the skeleton information only, this study aims at utilizing depth data and apply CNN to extract important spatial information from the depth images. Together, the tandem CNN+RNN is capable of recognizing a sequence of gestures more accurately. As well, various types of fusion are studied to combine both the skeleton and depth information in order to extract temporal-spatial information. An overall accuracy of 85.46% is achieved on the dynamic hand gesture-14/28 dataset.

This paper is concerned with mass evidence accumulation and risk assessment in a particular component of transportation systems and e-borders. We outline the challenges faced by contemporary border control technology and conduct a series of demonstrative experiments that cover critical scenarios, tasks, and states of both evidence accumulation and the traveler risk scoring engine. Using technology gap navigator methodology, this paper suggests an approach to traveler risk estimation based on a unified inference platform, such as a causal graphical model with various incorporated metrics of uncertainty.

In this paper, we present a novel tone mapping algorithm based on multi-scale histograms and fusion (MS-Hist), for displaying wide dynamic range (WDR) images and better detection of objects such as human faces. The proposed algorithm tone maps pixels based on multiple scale local histograms, where small scales are used to preserve local contrast and large scales allow to maintain the global brightness consistency. A database of WDR images of humans depicted in high-contrast light conditions was created to validate and compare the performance of various algorithms for face detection in tasks such as biometric based identification. Our experimental results show that the proposed MS-Hist algorithm preserves image detail, brightness and high local contrast, and can benefit tasks such as face detection in WDR images.

This paper concerns with facial-based watch list technology as a component of automated border control machines deployed in e-borders. The key task of the watch list technology is to mitigate effects of mis-identification and impersonation. To address this problem, we developed a novel cost-based model of traveler risk assessment and proved its efficiency via intensive experiments using large-scale facial databases. The results of this study are applicable to any biometric modality to be used in watch list technology.

Multispectral data from inexpensive, yet accurate, sensors has become readily available within the last several years and opened many possibilities for contactless biometrics applications. The Kinect v2 provides depth, RGB, and Near-Infrared (NIR) data and can be used for recognition of individuals using extracted hand regions in all three spectra. Initially, the depth data is used to extract the hand region for use as a mask to extract the hand region in the depth, RGB, and Near-Infrared (NIR) spectra. These extracted regions then have Principal Component Analysis (PCA) applied to them before passing through classification. K-Nearest-Neighbors (KNN) and Support Vector Machines (SVM) are compared for classification. In testing it was found that on average the RGB and NIR data provided a recognition rate of approximately 75%-80% for either KNN or SVM classification and at different amounts of principal components for PCA.

Facial biometrics, specifically facial expression analysis, is one of the most actively investigated topics towards the creation of an automated system capable of detecting and classifying pain in human subjects. This paper presents a comparative analysis of Gabor energy filter based approaches combined with powerful classifiers, such as Support Vector Machines, for pain detection and classification into three levels. The intensity of pain is labelled using the Prkachin and Solomon Pain Intensity scale. In this paper, the levels of intensity have been quantized into three disjoint groups: no pain, weak pain and strong pain. The results of experiments show that Gabor energy filters provide comparable or better results compared to previous filter-based pain recognition methods, with a 74% classification rate of pain versus no pain, and 74%, 30% and 78% precision rates when distinguishing pain into no pain, weak pain and strong pain respectively.

This paper revisits the concept of an authentication machine (A-machine) that aims at identifying/verifying humans. Although A-machines in the closed-set application scenario are well understood and commonly used for access control utilizing human biometrics (face, iris, and fingerprints), open-set applications of A-machines have yet to be equally characterized. This paper presents an analysis and taxonomy of A-machines, trends, and challenges of open-set real-world applications. This paper makes the following contributions to the area of open-set A-machines: 1) a survey of applications; 2) new novel life cycle metrics for theoretical, predicted, and operational performance evaluation; 3) a new concept of evidence accumulation for risk assessment; 4) new criteria for the comparison of A-machines based on the notion of a supporting assistant; and 5) a new approach to border personnel training based on the A-machine training mode. It offers a technique for modeling A-machines using belief (Bayesian) networks and provides an example of this technique for biometric-based e-profiling.

A novel Multi-Resolution Fusion (MRF) of Dual-Tree Complex Wavelet Transform (DTCWT) and Discrete Cosine Transform (DCT) is introduced in this paper. Shift invariant multi-scale feature set is obtained using 2D DTCWT. Subsequently, discriminant DCT coefficients are extracted to map the high dimensional features into low dimensional subspace. The resulting feature vector contains non-redundant discriminative information and is small in size. Therefore, the proposed face recognition technique exhibits computational efficiency, low storage requirement along with high recognition rate under varying shift conditions. It also provides robustness to expression and illumination change. The performance evaluation is accomplished on four standard face databases. Experimental results show significant performance improvement over existing well-established face recognition methods under varying conditions.

Majority of biometric researchers focus on the accuracy of matching using biometrics databases, including iris databases, while the scalability and speed issues have been neglected. In the applications such as identification in airports and borders, it is critical for the identification system to have low-time response. In this paper, a graph-based framework for pattern recognition, called Optimum-Path Forest (OPF), is utilized as a classifier in a pre-developed iris recognition system. The aim of this paper is to verify the effectiveness of OPF in the field of iris recognition, and its performance for various scale iris databases. This paper investigates several classifiers, which are widely used in iris recognition papers, and the response time along with accuracy. The existing Gauss-Laguerre Wavelet based iris coding scheme, which shows perfect discrimination with rotary Hamming distance classifier, is used for iris coding. The performance of classifiers is compared using small, medium, and large scale databases. Such comparison shows that OPF has faster response for large scale database, thus performing better than more accurate but slower Bayesian classifier.

Simulators of biometric data are emerging technologies for educational and training purposes (security access, forensic systems, public safety and health care). They emphasize decision-making skills in diverse situations. To model these situations or scenarios, synthetic biometric data can be used. This paper reviews an example of the application of synthetic biometrics for training users of a physical access control system. The modeling and simulation of biometric data is used for efficient support of security personnel training in dealing with customer identification under conditions of uncertainty. The other example is biomedical facilities such as remote monitoring of patient biometrics (physiological and behavioral patterns) in hospitals or care units. Such modeling requires developing specific training methodologies and techniques, including virtual environments.

This paper proposes integrating mutation analysis into model checking to improve coverage metrics of digital circuits. In contrast to traditional mutation testing where mutant faults are generated and injected into the code description of the model, we apply a series of newly defined mutation operators directly to the model properties rather than to the model code. We claim that any mutant properties that are generated from the initial properties and validated by the model checker should be considered as new properties that have been missed during the initial verification procedure. Therefore, adding these newly identified properties to the existing list of properties improves the coverage metric of the formal verification and consequently lead to a more reliable design. Preliminary simulation results of applying this approach to a 4x4 Booth-Multiplier with 6 and 8 initial properties, demonstrates a 40% and 45% coverage improvement respectively compared to the initial coverage metric.

A new facial biometric scheme is proposed in this paper. Three steps are included. First, a new nontensor product bivariate wavelet is utilized to get different facial frequency components. Then a modified 2D linear discriminant technique (M2DLD) is applied on these frequency components to enhance the discrimination of the facial features. Finally, support vector machine (SVM) is adopted for classification. Compared with the traditional tensor product wavelet, the new nontensor product wavelet can detect more singular facial features in the high-frequency components. Earlier studies show that the high-frequency components are sensitive to facial expression variations and minor occlusions, while the low-frequency component is sensitive to illumination changes. Therefore, there are two advantages of using the new nontensor product wavelet compared with the traditional tensor product one. First, the low-frequency component is more robust to the expression variations and minor occlusions, which indicates that it is more efficient in facial feature representation. Second, the corresponding high-frequency components are more robust to the illumination changes, subsequently it is more powerful for classification as well. The application of the M2DLD on these wavelet frequency components enhances the discrimination of the facial features while reducing the feature vectors dimension a lot. The experimental results on the AR database and the PIE database verified the efficiency of the proposed method.

3D face reconstruction is a popular area within the computer vision domain. 3D face reconstruction should ideally be achieved easily and cost-effectively, without requiring specialized equipment to estimate 3D shapes. As a result of this, many techniques for retrieving 3D shapes from 2D images have been proposed. In this paper, a novel method for 3D face reconstruction based on photometric stereo, which estimates the surface normal from shading information in multiple images, hence recovering the 3D shape of a face, is proposed. In order to overcome the problems of previous approaches related to prior-knowledge regarding lighting conditions and iterative algorithms, the exemplar is synthesized with known lighting conditions from at least three images, under arbitrary lighting conditions and using an illumination reference. Experiments in 3D face reconstruction were made by verifying the proposed approach using the illumination subset of the Max-Planck Institute face database and Yale face database B. Experimental results demonstrate that the proposed method is effective for 3D shape reconstruction of faces from 2D images.

This brief survey addresses the state-of-the-art techniques of inverse biometrics, which deals with synthesis of biometric data. It reports on genesis of synthetic biometric, advanced methods, and open application-specific problems. Currently deployed biometric systems use comprehensive methods and algorithms (such as pattern recognition, decision making, database searching, etc.) to analyze biometric data collected from individuals. We consider the inverse task, synthesis of artificial biometric data. These biologically meaningful data are useful, for example, for testing the biometric tools, and for enhancing the security of biometric systems. The synthetic data replicate all possible instances of otherwise unavailable data, thus, creating a variety of samples for testing. Properly created artificial biometric data provides a basis for enhancing security through the detailed and controlled modeling of a wide range of training skills, strategies and tactics of a hypothetical robber or forger. Databases of synthetic biometric data also serve for simulation in forensic systems.

This paper presents a concept of a new biometric-based physical access security system using an early warning principle and intelligent decision making support. This system is being prototyped in the first phase of a Project. The early warning principle exploits the real-time analysis of the biometrics of individual being scanned. Binding all sources of information into an objective supported by intelligence tools not only provide for reliable identification of an individual, but also supplies data for situational awareness and risk management support. An intelligent approach provides an interpretation of biometric data in semantic form, as well as dialogue support between the officer and the customer. The PASS is directed at a wide spectrum of applications such as immigration service and border control airports, seaports, and border-crossing, security of important public events, hospitals, and banking.

The above paper finds an optimal fixed-polarity Reed-Muller expansion of an n-variable totally symmetric function using an OFDD-based algorithm that requires O(n/sup 7/) time and O(n/sup 6/) storage space. However, an algorithm based on Suprun's transient triangles requires only O(n/sup 3/) time and O(n/sup 2/) storage space. An implementation of this algorithm yields computation times lower by several orders of magnitude.