Biometric recognition of children presents unique challenges due to rapid physiological changes that affect the consistency of extracted features over time. This study presents a longitudinal evaluation of a multimodal biometric system combining facial and ear features for child recognition across a three-year span. The evaluation uses collected datasets containing frontal and profile images of 231 children aged 3 to 18, with seven data collection sessions spaced at six-month intervals. Face recognition was performed using MagFace, while ear recognition was based on a pipeline involving Mask R-CNN for segmentation and an ensemble of VGG16 and MobileNet for feature extraction. Independent evaluations showed an increase in Equal Error Rate (EER) for face from 1.20% to 2.55% over a 36 -month interval, and for ear from $5.62 \%$ to $19.00 \%$ over the same period. When the two modalities were fused at the feature level, the system achieved improved stability with EER values ranging from $0.24 \%$ at 6 months to $1.38 \%$ at 36 months. Additional analysis across age groups revealed that children enrolled at younger ages experienced higher error rates, highlighting the importance of age-aware system design. These results demonstrate the effectiveness of face-ear fusion in supporting inclusive and temporally stable biometric systems, particularly for applications in education, healthcare, and identity tracking in humanitarian contexts.

Face recognition for infants and toddlers presents unique challenges due to rapid facial morphology changes, high inter-class similarity, and the limited availability of datasets. This study evaluates the performance of four deep learning-based face recognition models—FaceNet, ArcFace, Mag-Face, and CosFace—on a newly developed longitudinal dataset collected over a 24-month period in seven sessions involving children aged 0 to 3 years. Our analysis investigates recognition accuracy across multiple developmental stages, showing that the True Accept Rate (TAR) is only 30.7% at 0.1% False Accept Rate (FAR) for infants aged 0 to 6 months due to unstable facial features, but improves significantly in older children, reaching 64.7% TAR at 0.1% FAR in the 2.5 to 3 year age group. We also examine how face verification performance changes in different time intervals, revealing that shorter time gaps produce better accuracy due to reduced embedding drift. To mitigate this drift, we apply Domain-Adversarial Neural Network (DANN) strategy that improves TAR by more than 12% and yields features that are more temporally stable and generalizable. These findings are critical for building biometric systems that function reliably over time in smart city applications such as public healthcare, child safety, and digital identity services. The challenges observed in early age groups also highlight the importance of future research on privacy-preserving biometric authentication systems that can address temporal variability, especially in secure and regulated urban environments where child verification is vital.

LivDet-Iris 2025 is the sixth edition of the iris liveness detection competition. Held every two to three years, the competition aims to foster the development of robust algorithms capable of detecting a wide range of physically-and digitally-presented attacks in iris biometrics. The 2025 edition obtained the largest number of submissions in the history of the competition: ten algorithms from five institutions, and one commercial iris recognition system. LivDet-Iris 2025 also introduced new tasks compared to previous editions: (Task 1) a benchmark offered by an industry partner, (Task 2) morphed iris images, in which two different-identity samples were blended into one image, and (Task 3) evaluation of presentation attack detection robustness against advanced manufacturing techniques for textured contact lenses. This edition, for the first time in the series, offers a systematic testing of a commercial iris recognition system (software and hardware) using physical artifacts presented to the sensor. Dermalog-Iris team submitted algorithms that won all tasks, achieving the area under the ROC curve of 90.57%, 68.23% and 99.99% in tasks 1, 2, and 3, respectively. Additionally, we include results for baseline algorithms, based on modern deep convolutional neural networks and trained with all available public datasets of iris images representing bona fide samples and anomalies (physical attacks, eye diseases, post-mortem cases, and synthetically-generated iris images). Test samples created for tasks 2 and 3, and baseline models are made available to offer the state-of-the-art benchmark for iris liveness detection.

The Fingerprint Liveness Detection Competition (LivDet) is a recurring benchmark series that evaluates the effectiveness of software-based Presentation Attack Detection (PAD) algorithms in fingerprint recognition. LivDet2025 presents three challenges: (1) "Liveness Detection in Action", requiring the integration of PAD with user-specific recognition; (2) "Fingerprint Representation", evaluating the compactness and discriminability of feature vectors; and (3) "Adversarial Robustness", assessing the resilience of PADs to adversarially-crafted presentation attack instruments. This edition marks a significant milestone with the inclusion of contactless fingerprint data, promoting interoperability and robustness across acquisition technologies. Furthermore, no training data was provided; participants must select and declare external datasets for model development. The competition was open to academic and industrial research groups, with all submitted algorithms evaluated on common datasets and under standardized protocols. LivDet2025 aims to provide a comprehensive assessment of PAD performance under realistic, multi-sensor, and multi-attack scenarios. Results reveal important trade-offs between PAD accuracy, usability, and computational efficiency. For instance, some systems achieved high presentation attack rejection at the cost of extremely high false rejection rates, while others optimised speed and generalizability but exhibited limited attack resilience.

As more types of transactions move online, there is an increasing need to verify someone’s identity remotely. Remote identity verification (RIdV) technologies have emerged to fill this need. RIdV solutions typically use a smart device to validate an identity document like a driver’s license by comparing a face selfie to the face photo on the document. Recent research has been focused on ensuring that biometric systems work fairly across demographic groups. This study assesses five commercial RIdV solutions for equity across age, gender, race/ethnicity, and skin tone across 3,991 test subjects. This paper employs statistical methods to discern whether the RIdV result across demographic groups is statistically distinguishable. Two of the RIdV solutions were equitable across all demographics, while two RIdV solutions had at least one demographic that was in-equitable. For example, the results for one technology had a false negative rate of 10.5% +/- 4.5% and its performance for each demographic category was within the error bounds, and, hence, were equitable. The other technologies saw either poor overall performance or inequitable performance. For one of these, participants of the race Black/African American (B/AA) as well as those with darker skin tones (Monk scale 7/8/9/10) experienced higher false rejections. Finally, one technology demonstrated more favorable but inequitable performance for the Asian American and Pacific Islander (AAPI) demographic. This study confirms that it is necessary to evaluate products across demographic groups to fully understand the performance of remote identity verification technologies.

Imagine a world where a copy of your face could trick the most advanced security systems. This isn’t science fiction; it’s a real challenge today. LivDet-Face is a competition that aims to advance the detection of attacks at the biometric sensor, known as Presentation Attack Detection (PAD). This international contest is a key benchmark in biometric security, offering an unbiased look at the latest innovations in face PAD and demonstrating progress over time in detecting and preventing sophisticated attacks. Through the International Joint Conference on Biometrics (IJCB) platform, LivDet-Face 2024 provides a standardized evaluation process, access to advanced Presentation Attack Instruments (PAI), and a comprehensive dataset of bona fide face images. The competition had two main categories: algorithms and systems. A total of sixteen algorithms and one system were submitted for this year’s competition. Anonymous submissions topped both image and video subcategories with an ACER of 4.93% and 4.13%, respectively. In the systems category, Team Dermalog, despite being the sole submission, achieved an impressive ACER of 3.12%.

Modern face recognition (FR) models, particularly their convolutional neural network based implementations, often raise concerns regarding privacy and ethics due to their "black-box" nature. To enhance the explainability of FR models and the interpretability of their embedding space, we introduce in this paper three novel techniques for discovering semantically meaningful feature directions (or axes). The first technique uses a dedicated facial-region blending procedure together with principal component analysis to discover embedding space direction that correspond to spatially isolated semantic face areas, providing a new perspective on facial feature interpretation. The other two proposed techniques exploit attribute labels to discern feature directions that correspond to intra-identity variations, such as pose, illumination angle, and expression, but do so either through a cluster analysis or a dedicated regression procedure. To validate the capabilities of the developed techniques, we utilize a powerful template decoder that inverts the image embedding back into the pixel space. Using the decoder, we visualize linear movements along the discovered directions, enabling a clearer understanding of the internal representations within face recognition models. The source code will be made publicly available.

The need for reliable identification of children in various emerging applications has sparked interest in leveraging child face recognition technology. This study introduces a longitudinal approach to enrollment and verification accuracy for child face recognition, focusing on the YFA (Young Face Aging) database collected by Clarkson University’s CITeR research group over an 8-year period, at 6-month intervals. The dataset includes children ranging from 3 to 18 years of age, comprising 330 subjects with an average of 6 data collections per subject. Our research aims to comprehensively evaluate the performance of state-of-the- art face-matching techniques on the YFA database, assessing the feasibility of recognizing children's faces upon initial enrollment and verifying their identity longitudinally at 6-month intervals. We conduct a comprehensive analysis of the system’s accuracy considering multiple age groups. We also investigate the temporal degradation of face recognition accuracy over time. Notably, when comparing the initial enrollment image with longitudinal images over an 8-year period, we observe a decrease in accuracy. The average TAR across all age groups is 98.52% with a FAR of 0.1% with a 2-year age verification gap and drops to 95.68 with a 4-year age gap. However, this rate decreases to 87.24% after a time difference of 6 years and further drops to 71.32% with a time difference of 8 years. The highest drop in accuracy was noticed in the age group of (3-5) years old children and the lowest in (5.5-7) years old. By addressing the challenges and opportunities in child face recognition, this research contributes significantly to the advancement of technology for identifying missing or abducted children and other critical applications requiring dependable biometric recognition in children.

Advances in deep learning and convolutional neural networks (ConvNets) have driven remarkable face recognition (FR) progress recently. However, the black-box nature of modern ConvNet-based face recognition models makes it challenging to interpret their decision-making process, to understand the reasoning behind specific success and failure cases, or to predict their responses to unseen data characteristics. It is, therefore, critical to design mechanisms that explain the inner workings of contemporary FR models and offer insight into their behavior. To address this challenge, we present in this paper a novel template-inversion approach capable of reconstructing high-fidelity face images from the embeddings (templates, feature-space representations) produced by modern FR techniques. Our approach is based on a novel Deep Face Decoder (DFD) trained in a regression setting to visualize the information encoded in the embedding space with the goal of fostering explainability. We utilize the developed DFD model in comprehensive experiments on multiple unconstrained face datasets, namely Visual Geometry Group Face dataset 2 (VGGFace2), Labeled Faces in the Wild (LFW), and Celebrity Faces Attributes Dataset High Quality (CelebA-HQ). Our analysis focuses on the embedding spaces of two distinct face recognition models with backbones based on the Visual Geometry Group 16-layer model (VGG-16) and the 50-layer Residual Network (ResNet-50). The results reveal how information is encoded in the two considered models and how perturbations in image appearance due to rotations, translations, scaling, occlusion, or adversarial attacks, are propagated into the embedding space. Our study offers researchers a deeper comprehension of the underlying mechanisms of ConvNet-based FR models, ultimately promoting advancements in model design and explainability.

The ubiquity of mobile devices nowadays necessitates securing the apps and user information stored therein. However, existing one-time entry-point authentication mechanisms and enhanced security mechanisms such as Multi-Factor Authentication (MFA) are prone to a wide vector of attacks. Furthermore, MFA also introduces friction to the user experience. Therefore, what is needed is continuous authentication that once passing the entry-point authentication, will protect the mobile devices on a continuous basis by confirming the legitimate owner of the device and locking out detected impostor activities. Hence, more research is needed on the dynamic methods of mobile security such as behavioral biometrics-based continuous authentication, which is cost-effective and passive as the data utilized to authenticate users are logged from the phone's sensors. However, currently, there are not many mobile authentication datasets to perform benchmarking research. In this work, we share two novel mobile datasets (Clarkson University (CU) Mobile datasets I and II) consisting of multi-modality behavioral biometrics data from 49 and 39 users respectively (88 users in total). Each of our datasets consists of modalities such as swipes, keystrokes, acceleration, gyroscope, and pattern-tracing strokes. These modalities are collected when users are filling out a registration form in sitting both as genuine and impostor users. To exhibit the usefulness of the datasets, we have performed initial experiments on selected individual modalities from the datasets as well as the fusion of simultaneously available modalities.

As account compromises and malicious online attacks are on the rise, multi-factor authentication (MFA) has been adopted to defend against these attacks. OTP and mobile push notification are just two examples of the popularly adopted MFA factors. Although MFA improve security, they also add additional steps or hardware to the authentication process, thus increasing the authentication time and introducing friction. On the other hand, keystroke dynamics-based authentication is believed to be a promising MFA for increasing security while reducing friction. While there have been several studies on the usability of other MFA factors, the usability of keystroke dynamics has not been studied. To this end, we have built a web authentication system with the standard features of signup, login and account recovery, and integrated keystroke dynamics as an additional factor. We then conducted a user study on the system where 20 participants completed tasks related to signup, login and account recovery. We have also evaluated a new approach for completing the user enrollment process, which reduces friction by naturally employing other alternative MFA factors (OTP in our study) when keystroke dynamics is not ready for use. Our study shows that while maintaining strong security (0% FPR), adding keystroke dynamics reduces authentication friction by avoiding 66.3% of OTP at login and 85.8% of OTP at account recovery, which in turn reduces the authentication time by 63.3% and 78.9% for login and account recovery respectively. Through an exit survey, all participants have rated the integration of keystroke dynamics with OTP to be more preferable to the conventional OTP-only authentication.

Liveness Detection (LivDet) is an international competition series open to academia and industry with the objective to assess and report state-of-the-art in Presentation Attack Detection (PAD). LivDet-2023 Noncontact Fingerprint is the first edition of the noncontact fingerprint-based PAD competition for algorithms and systems. The competition serves as an important benchmark in noncontact-based fingerprint PAD, offering (a) independent assessment of the state-of-the-art in noncontact-based fingerprint PAD for algorithms and systems, and (b) common evaluation protocol, which includes finger photos of a variety of Presentation Attack Instruments (PAIs) and live fingers to the biometric research community (c) provides standard algorithm and system evaluation protocols, along with the comparative analysis of state-of-the-art algorithms from academia and industry with both old and new android smartphones. The winning algorithm achieved an APCER of 11.35% averaged over all PAIs and a BPCER of 0.62%. The winning system achieved an APCER of 13.0.4%, averaged over all PAIs tested over all the smartphones, and a BPCER of 1.68% over all smartphones tested. Four-finger systems that make individual finger-based PAD decisions were also tested. The dataset used for competition will be available1, to all researchers as per data share protocol.1https://noncontactfingerprint2023.1ivdet.org/index.php

This paper describes the results of the 2023 edition of the “LivDet” series of iris presentation attack detection (PAD) competitions. New elements in this fifth competition include (1) GAN-generated iris images as a category of presentation attack instruments (PAI), and (2) an evaluation of human accuracy at detecting PAI as a reference benchmark. Clarkson University and the University of Notre Dame contributed image datasets for the competition, composed of samples representing seven different PAI categories, as well as baseline PAD algorithms. Fraunhofer IGD, Beijing University of Civil Engineering and Architecture, and Hochschule Darmstadt contributed results for a total of eight PAD algorithms to the competition. Accuracy results are analyzed by different PAI types, and compared to human accuracy. Overall, the Fraunhofer IGD algorithm, using an attention-based pixel-wise binary supervision network, showed the best-weighted accuracy results (average classification error rate of 37.31%), while the Beijing University of Civil Engineering and Architecture’s algorithm won when equal weights for each PAI were given (average classification rate of 22.15%). These results suggest that iris PAD is still a challenging problem.

Keystroke dynamics has been shown to be a promising method for user authentication based on a user's typing rhythms. Over the years, it has seen increasing applications such as in preventing transaction fraud, account takeovers, and identity theft. However, due to the variable nature of keystroke dynamics, a user's typing patterns may vary on a different keyboard or in a different keyboard language setting, which may affect the system accuracy. In other words, an algorithm modeled with data collected using a mechanical keyboard may perform significantly differently when tested with an ergonomic keyboard. Similarly, an algorithm modeled with data collected in one language may perform significantly differently when tested with another language. Hence, there is a need to study the impact of multiple keyboards and multiple languages on keystroke dynamics performance. This motivated us to develop two free-text keystroke dynamics datasets. The first is a multi-keyboard keystroke dataset comprising of four (4) physical keyboards - mechanical, ergonomic, membrane, and laptop keyboards - and the second is a bilingual keystroke dataset in both English and Chinese languages. Data were collected from a total of 86 participants using a non-intrusive web-based keylogger in a semi-controlled setting. To the best of our knowledge, these are the first multi-keyboard and bilingual keystroke datasets, as well as the data collection software, to be made publicly available for research purposes. The usefulness of our datasets was demonstrated by evaluating the performance of two state-of-the-art free-text algorithms.

Facebook has become an important part of our daily life. From knowing the status of our relatives, showing off a new car, to connecting with a high school classmate, abundant personally identifiable information (PII) are made visible to others by posts, images and news. However, this free flow of information has also created significant cyber-security challenges that make us vulnerable to social engineering and cyber crimes. To confront these challenges, we propose a new behavioral biometric that verifies a user based on his or her widget interaction behavior when using Facebook. Specifically, we monitor activities on the user's Facebook account using our own logging software and verify the user's claimed identity by binary classifiers trained with two algorithms (SVM-rbf and the GBM- Gradient Boosting Machines). Our novel dataset consists of eight users over a month of data collection with an average of 2.95k rows of data per user. We convert these activities data into meaningful features such as day-of-week, hour-of-day, and widget types and duration of mouse staying on a widget. The performance shows that our novel widget interaction modality is promising for authentication. The SVM-rbf classifiers achieve a mean Equal Error Rate (EER) and mean Accuracy (ACC) of 3.91% and 97.79%, while the GBM classifiers a mean EER and ACC of 2.76% and 97.88%, respectively. In addition, we perform an ablation study to understand the impact of individual features on authentication performance. The importance of features are ranked in the descending order of hour-of-day, day-of-week, and widget types and duration.

Liveness Detection (LivDet)-Face is an international competition series open to academia and industry. The competition’s objective is to assess and report state-of-the-art in liveness / Presentation Attack Detection (PAD) for face recognition. Impersonation and presentation of false samples to the sensors can be classified as presentation attacks and the ability for the sensors to detect such attempts is known as PAD. LivDet-Face 2021 * will be the first edition of the face liveness competition. This competition serves as an important benchmark in face presentation attack detection, offering (a) an independent assessment of the current state of the art in face PAD, and (b) a common evaluation protocol, availability of Presentation Attack Instruments (PAI) and live face image dataset through the Biometric Evaluation and Testing (BEAT) platform. The competition can be easily followed by researchers after it is closed, in a platform in which participants can compare their solutions against the LivDet-Face winners.

In this work, we utilize progressive growth-based Generative Adversarial Networks (GANs) to develop the Clarkson Fingerprint Generator (CFG). We demonstrate that the CFG is capable of generating realistic, high fidelity, $512 \times 512$ pixels, full, plain impression fingerprints. Our results suggest that the fingerprints generated by the CFG are unique, diverse, and resemble the training dataset in terms of minutiae configuration and quality, while not revealing the underlying identities of the training data. We make the pre-trained CFG model and the synthetically generated dataset publicly available at https://github.com/keivanB/Clarkson_Finger_Gen

The dilation of the pupil and it's variation between a mated pair of irides has been found to be an important factor in the performance of iris recognition systems. Studies on adult irides indicated significant impact of dilation on iris recognition performance at different ages. However, the results of adults may not necessarily translate to children. This study analyzes dilation as a factor of age and over time in children, from data collected from same 209 subjects in the age group of four to 11 years at enrollment, longitudinally over three years spaced by six months. The performance of iris recognition is also analyzed in presence of dilation variation.

Launched in 2013, LivDet-Iris is an international competition series open to academia and industry with the aim to assess and report advances in iris Presentation Attack Detection (PAD). This paper presents results from the fourth competition of the series: LivDet-Iris 2020. This year's competition introduced several novel elements: (a) incorporated new types of attacks (samples displayed on a screen, cadaver eyes and prosthetic eyes), (b) initiated LivDet-Iris as an on-going effort, with a testing protocol available now to everyone via the Biometrics Evaluation and Testing (BEAT)* open-source platform to facilitate reproducibility and benchmarking of new algorithms continuously, and (c) performance comparison of the submitted entries with three baseline methods (offered by the University of Notre Dame and Michigan State University), and three open-source iris PAD methods available in the public domain. The best performing entry to the competition reported a weighted average APCER of 59.10% and a BPCER of 0.46% over all five attack types. This paper serves as the latest evaluation of iris PAD on a large spectrum of presentation attack instruments.

Following the development of computing and communication technologies, the idea of Internet of Things (IoT) has been realized not only at research level but also at application level. Among various IoT-related application fields, biometrics applications, especially face recognition, are widely applied in video-based surveillance, access control, law enforcement and many other scenarios. In this paper, we introduce a Face in Video Recognition (FivR) framework which performs real-time key-frame extraction on IoT edge devices, then conduct face recognition using the extracted key-frames on the Cloud back-end. With our key-frame extraction engine, we are able to reduce the data volume hence dramatically relief the processing pressure of the cloud back-end. Our experimental results show with IoT edge device acceleration, it is possible to implement face in video recognition application without introducing the middle-ware or cloud-let layer, while still achieving real-time processing speed.

Conventional one-stop authentication of a computer terminal takes place at a user's initial sign-on. In contrast, continuous authentication protects against the case where an intruder takes over an authenticated terminal or simply has access to sign-on credentials. Behavioral biometrics has had some success in providing continuous authentication without requiring additional hardware. However, further advancement requires benchmarking existing algorithms against large, shared datasets. To this end, we provide a novel large dataset that captures not only keystrokes, but also mouse events and active programs. Our dataset is collected using passive logging software to monitor user interactions with the mouse, keyboard, and software programs. Data was collected from 103 users in a completely uncontrolled, natural setting, over a time span of 2.5 years. We apply Gunetti & Picardi's algorithm, a state-of-the-art algorithm in free text keystroke dynamics, as an initial benchmarkfor the new dataset.

Presentation attacks such as using a contact lens with a printed pattern or printouts of an iris can be utilized to bypass a biometric security system. The first international iris liveness competition was launched in 2013 in order to assess the performance of presentation attack detection (PAD) algorithms, with a second competition in 2015. This paper presents results of the third competition, LivDet-Iris 2017. Three software-based approaches to Presentation Attack Detection were submitted. Four datasets of live and spoof images were tested with an additional cross-sensor test. New datasets and novel situations of data have resulted in this competition being of a higher difficulty than previous competitions. Anonymous received the best results with a rate of rejected live samples of 3.36% and rate of accepted spoof samples of 14.71%. The results show that even with advances, printed iris attacks as well as patterned contacts lenses are still difficult for software-based systems to detect. Printed iris images were easier to be differentiated from live images in comparison to patterned contact lenses as was also seen in previous competitions.

The pervasiveness of smartphones equipped with various built-in sensors combined with the capability of serving multiple applications that could access social network information introduces next generation soft biometrics tools that could be used to verify a user's identity through their social behavior. Smart mobile devices can provide multi-modal data acquisition from various social networking applications, and when aggregated, these data can help form highly identifiable behaviometric information. Continuous identification and authentication of users through monitoring social behavior improves detection of identity spoofing. In this paper, we propose a social behaviometric framework to cope with identity spoofing on smartphones. The proposed framework consists of a front-end client module that acquires and provides social networking data to the back-end module which runs online machine learning procedures and provides analytics as a service to the front-end in order to verify user identity through social interactions. We evaluate the performance of the proposed framework by using real data collected from participants, and inject noisy behavioral patterns to simulate identity spoofing scenarios. Performance results show that under anomalous behavioral patterns, the proposed system can identify genuine users with up to 97% success ratio using an aggregated behavior pattern on five different social network applications.

The widespread use of mobile technology has accelerated the popularity of social networking services, and has made these services convenient to access. This paper presents a behaviometric mobile application, namely TrackMaison (Track My activity in social networks). TrackMaison keeps track of social network service usage of smartphone users through data usage, location, usage frequency and session duration of five popular social network services. The data collected by the mobile application is presented to the smartphone user and is analyzed to aid in understanding mobile social network service usage. Furthermore, we introduce the social activity rate and sociability factor metrics where the former is a function of a user's relative data usage rate in social network services and the latter is a function of a user's relative session durations in social networks. By using TrackMaison tool, we identify three user behavior types. Those are active user profile, moderately active user profile and low active user profile. Through analysis of real data, we advocate that continuous identification/authentication of mobile device users is possible by using the introduced sociability metrics. We further present a case study on various Instagram user profiles, and show that low active profiles can be identified with negligible false acceptance rates (FAR) whereas a highly active user can be identified with a FAR as low as 3%.

Keystroke authentication can help significantly improve computer security by hardening passwords or offering active, continuous authentication. Over the years, many keystroke authentication algorithms have been reported to produce promising results. However, these results are tested on proprietary datasets with varying numbers of subjects and amounts of text, making it difficult to compare and improve the state of art. We describe a new dataset that we have developed with the goal to serve as a shared common testbed to enable future improvements. The new dataset includes keystroke data for short pass-phrases, fixed text (transcription of long proses), and free text. It also includes video of a subject's facial expression and hand movement during the data collection sessions, allowing for a deeper understanding of why an algorithm works the way it does, for example, by finding out whether a subject is a touchtypist or not. As a baseline for benchmarking, we also include the results of replicating two existing algorithms using the new dataset.

The use of an artificial replica of a biometric characteristic in an attempt to circumvent a system is an example of a biometric presentation attack. Liveness detection is one of the proposed countermeasures, and has been widely implemented in fingerprint and iris recognition systems in recent years to reduce the consequences of spoof attacks. The goal for the Liveness Detection (LivDet) competitions is to compare software-based iris liveness detection methodologies using a standardized testing protocol and large quantities of spoof and live images. Three submissions were received for the competition Part 1; Biometric Recognition Group de Universidad Autonoma de Madrid, University of Naples Federico II, and Faculdade de Engenharia de Universidade do Porto. The best results from across all three datasets was from Federico with a rate of falsely rejected live samples of 28.6% and the rate of falsely accepted fake samples of 5.7%.

Biometric applications become paramount across private sectors, industry, as well as government agencies. As large amount of data being collected from many different sources, managing such volumes of data and developing efficient and effective large-scale operational solutions are becoming a concern. For example, real-time identification of individuals with the purpose of allowing or denying their access to specific system or resource is challenging from the performance point of view. In addition, processing large amount of data would definitely consume a significant amount of energy. The Single-chip Cloud Computer (SCC) is an experimental processor created by Intel Labs. In this paper we employ SCC, which supports dynamic frequency and voltage scaling (DVFS), to investigate the power-aware computing and performance enhancement of an iris matching algorithm on such many-core architecture. This biometric application contains a large degree of parallelism that we can exploit by porting it onto the SCC. Results in terms of performance, power, energy, energy delay product (EDP), and power per speedup (PPS) metrics of executing the iris matching application under different number of cores, frequency, and voltage settings of the SCC platform are presented. We also analyze how the results for these metrics vary as we change these parameters.

Under varying illumination, both the statistical and structural contents of color texture are modified, leading to changes in the observed texture surface. We model the effect of illumination as a perturbation on an ideal color texture and show that the spectra of the ambient light have a significant impact on the observed texture patterns in the individual color channels. Motivated by studies in human color constancy, we propose a correlation-based transformation that minimizes the effect of illumination variation in color texture analysis. Experimental results are included, which validate the performance of the proposed minvariance model in the analysis of color texture.

Multimodal systems have been used for the increased robustness of biometric recognition tasks. A unique strength of multimodal systems can be found when presented with biometric samples of degraded quality in a subset of the modalities. This study looks at the effect of quality degradation on system performance using the Q-FIRE database. The Q-FIRE database is a multimodal database composed of face and iris biometrics captured at defined quality levels, controlled at acquisition. This database allows for assessment of biometric system performance pertaining to image quality factors. Methods for measuring image quality based on illumination conditions are explored as well as strategies for incorporating these quality metrics into a multimodal fusion algorithm. This paper provides further evidence in a unique dataset that utilizing sample quality metrics into the fusion scheme of a multimodal system improves system performance in non-ideal acquisition environments.

Several recent advancements in the field of texture analysis prompt some fundamental questions. For instance, what is the true impact of these novel advancements under real-world environments? When do these novel advancements fail to perform? Which methods perform better and under what conditions? In this work, we investigate these and other issues under nonideal image acquisition environments, specifically, environments with changing conditions due to illumination variations and those caused by both affine and nonaffine transformations. We study the performance of nine popular texture analysis algorithms using three different datasets, with varying levels of difficulty. Experiments are performed on nonideal texture datasets under five different setups. We find that most state-of-the-art techniques do not perform well under these conditions. To a large extent, their performance under nonideal conditions depends critically on the nature of the textural surface. Moreover, most techniques fail to perform reliably when the number of classes in the dataset is increased significantly, over the regular-size datasets used in previous work. Multiscale features performed reasonably well against variations caused by illumination and rotation but are prone to fail under changes in scale. Surprisingly, the performance for most of the algorithms is generally stable on structured or periodic textures, even with variations in illumination or affine transformations.

Level-3 fingerprint features from fingerprint images like pores are difficult to capture detect, and involve high resolution scanners with higher ppi count. However, these features provide finer information about a fingerprint characteristics. Furthermore, fingerprint pores may be useful in determining liveness of fingerprint in order to prevent spoofing of fingerprint devices. In this study fingerprint pores along the ridges are used for fingerprint matching. Wavelet based fingerprint enhancement techniques are implemented to ease detection of the level-3 features. Delaunay triangulation based alignment and matching of the fingerprints is performed. The pores are checked for the liveness by perspiration activity in the time series captures. The developed matching scheme is tested for the high resolution data (686 ppi) for 114 live and spoof fingerprint classes. ROC is plotted and EER of 2.97% is obtained.

In this paper we propose a sound recognition technique based on the limited receptive area (LIRA) neural classifier and short-time Fourier transform (ST FT). LIRA neural classifier was developed as a multipurpose image recognition system. Previous tests of LIRA demonstrated good results in different image recognition tasks including: handwritten digit recognition, face recognition, metal surface texture recognition, and micro work piece shape recognition. We propose a sound recognition technique where spectrograms of sound instances serve as inputs of the LIRA neural classifier. The methodology was tested in recognition of swallowing sounds. Swallowing sound recognition may be employed in systems for automated swallowing assessment and diagnosis of swallowing disorders. The experimental results suggest high efficiency and reliability of the proposed approach.

The popularity of the iris biometric has grown considerably over the past two to three years. Most research has been focused on the development of new iris processing and recognition algorithms for frontal view iris images. However, a few challenging directions in iris research have been identified, including processing of a nonideal iris and iris at a distance. In this paper, we describe two nonideal iris recognition systems and analyze their performance. The word "nonideal" is used in the sense of compensating for off-angle occluded iris images. The system is designed to process nonideal iris images in two steps: 1) compensation for off-angle gaze direction and 2) processing and encoding of the rotated iris image. Two approaches are presented to account for angular variations in the iris images. In the first approach, we use Daugman's integrodifferential operator as an objective function to estimate the gaze direction. After the angle is estimated, the off-angle iris image undergoes geometric transformations involving the estimated angle and is further processed as if it were a frontal view image. The encoding technique developed for a frontal image is based on the application of the global independent component analysis. The second approach uses an angular deformation calibration model. The angular deformations are modeled, and calibration parameters are calculated. The proposed method consists of a closed-form solution, followed by an iterative optimization procedure. The images are projected on the plane closest to the base calibrated plane. Biorthogonal wavelets are used for encoding to perform iris recognition. We use a special dataset of the off-angle iris images to quantify the performance of the designed systems. A series of receiver operating characteristics demonstrate various effects on the performance of the nonideal-iris-based recognition system.

It has been demonstrated that simple and inexpensive techniques are sufficient to spoof fingerprint scanners. Previously, effective use of physiological phenomenon of perspiration is shown as a counter-measure against such attacks. These techniques require more than one image for performing the liveness check and hence may not be suited for on-line processing. In this work, a liveness measure based on single image is developed. The inherent texture and density differences between `live' and `not live' fingerprint images are exploited. Multiresolution texture analysis techniques are used to minimize the energy associated with phase and orientation maps. Cross ridge frequency analysis of fingerprint images is performed by means of statistical measures and weighted mean phase is calculated. These different features along with ridge reliability or ridge center frequency are given as inputs to a fuzzy c-means classifier. The proposed algorithm was applied to a dataset of approximately 58 live, 50 spoof and 28 cadaver fingerprint images, from three different types of scanners. An error rate of 1.4% is achieved. The algorithm provides a faster technique for doing a liveness test which relies on only one fingerprint image.

Heart rate, heart rate variability (HRV), and sleep state are some of the common physiologic parameters used in studies of infants. HRV is easily derived from infant electrocardiograms (ECG), but sleep state scoring is a time consuming task using many physiological signals. We propose a technique to reliably determine sleep and wake using only the ECG. The method would be tested with simultaneous ECG and polysomnograph (PSG) determined sleep scores from the Collaborative Home Infant Monitoring Evaluation (CHIME) study. The advantages include high accuracy, simplicity of use, and low intrusiveness, with design including rejection to increase reliability valuable for determining sleep-wake states in highly sensitive groups such as infants. Learning vector quantization and multi-layer perceptron (MLP) neural networks are tested as the predictors. The manual PSG scored test set has 38,121 (67.8%) sleep and 18,076 (32.2%) wake epochs for a total of 56,197 epochs. The MLP classification of the entire test set resulted in 77.7% agreement with the PSG sleep epochs and 79.0% with wake. The rejection scheme applied to the MLP resulted in 28.9% of sleep and wake epochs meeting the rejection criterion. Of the remaining 39,946 epochs 86.0% are in agreement with the PSG sleep epochs and 85.4% with wake. After systematic rejection of difficult to classify segments, this model can achieve 85%-86% correct classification while rejecting only 30% of the data. This is an improvement of about 7.8% over a traditional model without rejection

Fingerprint scanners may be susceptible to spoofing using artificial materials, or in the worst case, dismembered fingers. An anti-spoofing method based on liveness detection has been developed for use in fingerprint scanners. This method quantifies a specific temporal perspiration pattern present in fingerprints acquired from live claimants. The enhanced perspiration detection algorithm presented here improves our previous work by including other fingerprint scanner technologies; using a larger, more diverse data set; and a shorter time window. Several classification methods were tested in order to separate live and spoof fingerprint images. The dataset included fingerprint images from 33 live subjects, 33 spoofs created with dental material and Play-Doh, and fourteen cadaver fingers. Each method had a different performance with respect to each scanner and time window. However, all the classifiers achieved approximately 90% classification rate for all scanners, using the reduced time window and the more comprehensive training and test sets.

A method for temporal pattern recognition for continuous time signals is addressed. It is shown how a simple form of back-propagation can be used in conjunction with a temporal error signal to adapt both the weights and path delays of a continuous time delay feed forward multi-layer neural network with hard-limited output. An instance of such a network is simulated and some of the results are discussed. During the initial tests the network showed robust capabilities for detection of temporal patterns, including fast recognition of onsets of new waveforms in presence of moderately heavy noise and phase and frequency distortions.