Although face recognition systems have undergone an impressive evolution in the last decade, these technologies are vulnerable to attack presentations (AP). These attacks are mostly easy to create and, by executing them against the system’s capture device, the malicious actor can impersonate an authorised subject and thus gain access to the latter’s information (e.g., financial transactions). To protect facial recognition schemes against presentation attacks, state-of-the-art deep learning presentation attack detection (PAD) approaches require a large amount of data to produce reliable detection performances and even then, they decrease their performance for unknown presentation attack instruments (PAI) or database (information not seen during training), i.e. they lack generalisability. To mitigate the above problems, this paper focuses on zero-shot PAD. To do so, we first assess the effectiveness and generalisability of foundation models in established and challenging experimental scenarios and then propose a simple but effective framework for zero-shot PAD. Experimental results show that these models are able to achieve performance in difficult scenarios with minimal effort of the more advanced PAD mechanisms, whose weights were optimised mainly with training sets that included APs and bona fide presentations. The top-performing foundation model outperforms by a margin the best from the state of the art observed with the leaving-one-out protocol on the SiW-Mv2 database, which contains challenging unknown 2D and 3D attacks.11https://github.com/ljsoler/zero-shot-FoundationPAD

Foundation models are becoming increasingly popular due to their strong generalization capabilities resulting from being trained on huge datasets. These generalization capabilities are attractive in areas such as NIR Iris Presentation Attack Detection (PAD), in which databases are limited in the number of subjects and diversity of attack instruments, and there is no correspondence between the bona fide and attack images because, most of the time, they do not belong to the same subjects. This work explores an iris PAD approach based on two foundation models, DinoV2 and OpenClip. The results show that fine-tuning prediction with a small neural network as head overpasses the state-of-the-art performance based on deep learning approaches. However, systems trained from scratch have still reached better results if bona fide and attack images are available.

Face morphing attacks threaten the integrity of biometric identity systems by enabling multiple individuals to share a single identity. To develop and evaluate effective morphing attack detection (MAD) systems, we need access to high-quality, realistic morphed images that reflect the challenges posed in real-world scenarios. However, existing morph generation methods often produce images that are blurry, riddled with artifacts, or poorly constructed—making them easy to detect and not representative of the most dangerous attacks. In this work, we introduce StableMorph, a novel approach that generates highly realistic, artifact-free morphed face images using modern diffusion-based image synthesis. Unlike prior methods, StableMorph produces full-head images with sharp details, avoids common visual flaws, and offers unmatched control over visual attributes. Through extensive evaluation, we show that StableMorph images not only rival or exceed the quality of genuine face images, but also maintain a strong ability to fool face recognition systems—posing a greater challenge to existing MAD solutions and setting a new standard for morph quality in research and operational testing. StableMorph improves the evaluation of biometric security by creating more realistic and effective attacks and supports the development of more robust detection systems.

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.

Biometric capture devices have been utilised to estimate a person’s alertness through near-infrared iris images, expanding their use beyond just biometric recognition. However, capturing a substantial number of corresponding images related to alcohol consumption, drug use, and sleep deprivation to create a dataset for training an AI model presents a significant challenge. Typically, a large quantity of images is required to effectively implement a deep learning approach. Currently, training downstream models with a huge number of images based on foundational models provides a real opportunity to enhance this area, thanks to the generalisation capabilities of self-supervised models. This work examines the application of deep learning and foundational models in predicting fitness for duty, which is defined as the subject condition related to determining the alertness for work.

This work summarises and reports the results of the second Presentation Attack Detection competition on ID cards. This new version includes new elements compared to the previous one. (1) An automatic evaluation platform was enabled for automatic benchmarking; (2) Two tracks were proposed in order to evaluate algorithms and datasets respectively; and (3) A new ID card dataset was shared with Track 1 teams to serve as the baseline dataset for the training and optimisation. The Hochschule Darmstadt, Fraunhofer-IGD, and Facephi company jointly organised this challenge. 20 teams were registered, and 74 submitted models were evaluated. For Track 1, the "Dragons" team reached first place with an Average Ranking and Equal Error rate (EER) of (AVRank) of 40.48% and 11.44% EER, respectively. For the more challenging approach in Track 2, the "Incode" team reached the best results with an AVRank of 14.76% and 6.36% EER, improving on the results of the first edition of 74.30% and 21.87% EER, respectively. These results suggest that PAD on ID cards is improving, but it is still a challenging problem related to the number of images, especially of bona fide images.

The demand for presentation attack detection (PAD) to identify fraudulent identity documents in remote verification systems has experienced considerable expansion in recent years. This increase is the result of several factors, such as the rise of remote working, online shopping, migration and advances in synthetic imaging. Additionally, an increase in the number of attacks targeting the enrollment process has been noted. Training a PAD system to detect fraudulent identity documents is challenging due to the limited number of available identity documents, as collecting identity passports raises privacy concerns. To address the scarcity of available data, this work proposes a new, realistic ICAO-compliant passport dataset generated using a novel hybrid method that combines synthetic data with open-access information. Experimental evaluation in challenging environments validates the utility of synthetic data. A commercial off-the-shelf PAD algorithm trained on real data computes a detection equal error rate of 19% when using the proposed synthetic dataset for testing1.

Face morphing attack detection is challenging and presents a concrete and severe threat to face verification systems. A reliable detection mechanism for such attacks, tested with a robust cross-dataset protocol and unknown morphing tools, is still a research challenge. This paper proposes a framework based on the Few-Shot-Learning approach that shares image information based on the Siamese network using triplet-semi-hard-loss to tackle the morphing attack detection and boost the learning classification process. This network compares a bona fide or potentially morphed image with triplets of morphing face images. Our results show that this new network clusters the morphed images and assigns them to the right classes to obtain a lower equal error rate in a cross-dataset scenario. Few-shot learning helps to boost the learning process by sharing only small image numbers from an unknown dataset. Experimental results using cross-datasets trained with FRGCv2 and tested with FERET datasets reduced the BPCER 10 from 43% to 4.91% using ResNet50. For the AMSL open-access dataset is reduced for MobileNetV2 from BPCER 10 of 31.50% to 2.02%. For the SDD open-access synthetic dataset, the BPCER 10 is reduced for MobileNetV2 from 21.37% to 1.96%.

In the last few years, face morphing [1], [2] attacks has been shown to be a complex challenge for Face Recognition Systems (FRSs). Thus, the evaluation of other biometric modalities such as fingerprint, iris, and others must be explored and evaluated to enhance biometric systems. This work proposes an end-to-end framework to produce iris morphs at the image level, creating morphs from periocular iris images. This framework considers different stages such as iris pair selection from different subjects, segmentation, morph creation, and a new iris recognition system. In order to create realistic morphed images, two approaches for subject selection are proposed: random selection and similar pupil radius size selection. A vulnerability analysis and a Single Morphing Attack Detection algorithm were also explored. The results show that this approach obtained very realistic images that can confuse conventional iris recognition systems.

Numerous studies have shown that existing Face Recognition Systems (FRS), including commercial ones, often exhibit biases toward certain ethnicities due to under-represented data. In this work, we explore ethnicity alteration and skin tone modification using synthetic face image generation methods to increase the diversity of datasets. We conduct a detailed analysis by first constructing a balanced face image dataset representing three ethnicities: Asian, Black, and Indian. We then make use of existing Generative Adversarial Network-based (GAN) image-to-image translation and manifold learning models to alter the ethnicity from one to another. A systematic analysis is further conducted to assess the suitability of such datasets for FRS by studying the realistic skin-tone representation using Individual Typology Angle (ITA). Further, we also analyze the quality characteristics using existing Face image quality assessment (FIQA) approaches. We then provide a holistic FRS performance analysis using four different systems. Our findings pave the way for future research works in (i) developing both specific ethnicity and general (any to any) ethnicity alteration models, (ii) expanding such approaches to create databases with diverse skin tones, (iii) creating datasets representing various ethnicities which further can help in mitigating bias while addressing privacy concerns.

Face recognition systems are widely deployed in high-security applications such as for biometric verification at border controls. Despite their high accuracy on pristine data, it is well-known that digital manipulations, such as face morphing, pose a security threat to face recognition systems. Malicious actors can exploit the facilities offered by the identity document issuance process to obtain identity documents containing morphed images. Thus, subjects who contributed to the creation of the morphed image can with high probability use the identity document to bypass automated face recognition systems. In recent years, no-reference (i.e., single image) and differential morphing attack detectors have been proposed to tackle this risk. These systems are typically evaluated in isolation from the face recognition system that they have to operate jointly with and do not consider the face recognition process. Contrary to most existing works, we present a novel method for adapting deep learning-based face recognition systems to be more robust against face morphing attacks. To this end, we introduce TetraLoss, a novel loss function that learns to separate morphed face images from its contributing subjects in the embedding space while still achieving high biometric verification performance. In a comprehensive evaluation, we show that the proposed method can significantly enhance the original system while also significantly outperforming other tested baseline methods.

Morphing attacks blend face images of multiple distinct identities into a single photo combining their facial characteristics. Since the morphed image can be verified to multiple identities, these attacks greatly threaten face recognition systems. Morphing Attack Detection (MAD), addresses this problem by determining if an image is a bona fide or morphed image. Previous works on MAD focused on the models’ decision to improve their performance and generalizability. However, it has not been investigated whether the estimated probabilities of such decisions accurately reflect the true confidence with which predictions are made. Since these models struggle with unseen attacks, it is important to determine the models’ decision confidence to decide to trust or distrust the decisions. In this work, we (a) demonstrate that state-of-the-art MAD models struggle with providing reliable confidence estimations, (b) propose two new metrics to categorize their confidence prediction behavior, and (c) demonstrate that a simple calibration method can make the model more trustworthy without changing general model performance. The experiments were conducted in cross-dataset evaluation settings across two different MAD models using three different preprocessing and five morphing generation techniques. We found that both models are not calibrated and therefore not trustworthy. Moreover, it is shown that a simple and effective adjustment of the prediction confidence is possible. This will make future presentation attack detection, and especially MAD, models more trustworthy.

The morphing attack is widely acknowledged as an important security threat to face recognition systems in the context of electronic machine readable travel documents and several possible countermeasures have been recently proposed. Among the existing solutions, differential Morphing Attack Detection (MAD) algorithms, based on the comparison of the document image (possibly morphed) and a trusted live capture, proved to be quite effective and robust in detecting this kind of attack. However, deploying such solutions in a real-world operational scenario requires the capability of dealing with images of variable quality in terms of illumination, pose, focus, etc. This paper analyzes the impact of face image quality on MAD performance through an extensive image quality assessment, carried out on a large and realistic operational dataset using different state-of-the-art algorithms, thus providing useful insights for the development of more robust MAD systems.

Face morphing attacks pose a severe security threat to face recognition systems, enabling the morphed face image to be verified against multiple identities. To detect such manipulated images, the development of new face morphing methods becomes essential to increase the diversity of training datasets used for face morph detection. In this study, we present a representation-level face morphing approach, namely LADIMO, that performs morphing on two face recognition embeddings. Specifically, we train a Latent Diffusion Model to invert a biometric template - thus reconstructing the face image from an FRS latent representation. Our subsequent vulnerability analysis demonstrates the high morph attack potential in comparison to MIPGAN-II, an established GAN-based face morphing approach. Finally, we exploit the stochastic LADMIO model design in combination with our identity conditioning mechanism to create unlimited morphing attacks from a single face morph image pair. We show that each face morph variant has an individual attack success rate, enabling us to maximize the morph attack potential by applying a simple re-sampling strategy. We will publish our code and pre-trained models upon the acceptance of this paper.

Acquiring face images of sufficiently high quality is important for online ID and travel document issuance applications using face recognition systems (FRS). Low-quality, manipulated (intentionally or unintentionally), or distorted images degrade the FRS performance and facilitate documents’ misuse. Securing quality for enrolment images, especially in the unsupervised self-enrolment scenario via a smartphone, becomes important to assure FRS performance. In this work, we focus on the less studied area of radial distortion (a.k.a., the fish-eye effect) in face images and its impact on FRS performance. We introduce an effective radial distortion detection model that can detect and flag radial distortion in the enrolment scenario. We formalize the detection model as a face image quality assessment (FIQA) algorithm and provide a careful inspection of the effect of radial distortion on FRS performance. Evaluation results show excellent detection results for the proposed models, and the study on the impact on FRS uncovers valuable insights into how to best use these models in operational systems.

Biometric Sample Quality Assessment (BSQA) estimates the usefulness of the captured image based on its utility for the recognition task. In this regard, the majority of studies have been proposed in the last decade for computing a sample quality score from facial images. In particular, methods that learn a regressor from pseudo-labels have obtained reliable results on various benchmarks. However, they fail to correctly estimate the quality of samples having both, low quality and low intra-class variability. This paper proposes a new BSQA approach, Perceptual Classifiability Ratio for Hand Image Quality Assessment (PCR-HIQA), which computes hand image quality by combining the relative classifiability of the sample with its fidelity-related properties. On the one hand, the classifiability ratio is calculated by mapping the feature representation of the training samples in the angular space with respect to its class centroid to the nearest negative class centroid. On the other hand, the fidelity properties encode the human perception of the input sample quality. Experimental results on the challenging HaGRID database, containing different hand gestures, underline the superiority of the proposed BSQA method which outperforms state-of-the-art techniques by up to 30%.1

This paper summarises the Competition on Presentation Attack Detection on ID Cards (PAD-IDCard) held at the 2024 International Joint Conference on Biometrics (IJCB 2024). The competition attracted a total of ten registered teams, both from academia and industry. In the end, the participating teams submitted five valid submissions, with eight models to be evaluated by the organisers. The competition presented an independent assessment of current state-of-the-art algorithms. Today, no independent evaluation on cross-dataset is available; therefore, this work determined the state-of-the-art on ID cards. To reach this goal, a sequestered test set and baseline algorithms were used to evaluate and compare all the proposals. The sequestered test dataset contains ID cards from four different countries. In summary, a team that chose to be "Anonymous" reached the best average ranking results of 74.80%, followed very closely by the "IDVC" team with 77.65%.

Various face image datasets intended for facial biometrics research were created via web-scraping, i.e. the collection of images publicly available on the internet.This work presents an approach to detect both exactly and nearly identical face image duplicates, using file and image hashes.The approach is extended through the use of face image preprocessing.Additional steps based on face recognition and face image quality assessment models reduce false positives, and facilitate the deduplication of the face images both for intra-and inter-subject duplicate sets.The presented approach is applied to five datasets, namely LFW, TinyFace, Adience, CASIA-WebFace, and C-MS-Celeb (a cleaned MS-Celeb-1M variant).Duplicates are detected within every dataset, with hundreds to hundreds of thousands of duplicates for all except LFW.Face recognition and quality assessment experiments indicate a minor impact on the results through the duplicate removal.

It has been shown that digitally manipulated face images can pose a security threat to automated authentication systems (e.g., when such systems are used for border control). In such scenarios, a malicious actor can, in many countries, apply for an identity document using a manipulated face image, which can then be used to gain fraudulent access to a system. Research has shown that humans and algorithms struggle to detect digitally manipulated face images, especially when the type of manipulation is unknown or when evaluated across multiple types of manipulations. In this work, we consider the detection performance of algorithms and humans on datasets consisting of retouched, face swapped and morphed images. Specifically, we investigate the joint performance of algorithms and humans in a differential detection scenario where both a trusted and suspected image are presented simultaneously. To this end, we propose a conditional face image manipulation detection approach where the human decision is only considered when the algorithm is unsure about the decision outcome. The results show that the automated algorithm performs better than the human detectors and that combining the decisions of algorithms and humans, in general, leads to an increased detection performance. To our knowledge, this is the first study to explore the joint detection performance of algorithms and humans in a differential face manipulation detection scenario and when using a variety of face image manipulations.

This paper proposes a new method to estimate behavioural curves from Near-Infra-Red (NIR) iris images for classifying Fitness for Duty using a biometric capture device. Fitness for Duty (FFD) techniques detect whether a subject is Fit to safely perform a given task, which means no reduced alertness condition and security, or the subject is unfit, that could impact a reduced alertness condition by sleepiness or consumption of alcohol and drugs. The analysis showed essential differences in pupil and iris behaviour to classify the workers in “Fit” or “Unfit” conditions. The best results can distinguish subjects robustly under alcohol, drug consumption, and sleep conditions. The Multi-Layer-Perceptron and Gradient Boosted Machine reached the best results in all groups with an overall accuracy for Fit and Unfit classes of 74.0% and 75.5%, respectively. These results open a new application for iris capture devices.

Deep neural networks have become prevalent in human analysis, boosting the performance of applications, such as biometric recognition, action recognition, as well as person re-identification. However, the performance of such networks scales with the available training data. In human analysis, the demand for large-scale datasets poses a severe challenge, as data collection is tedious, time-expensive, costly and must comply with data protection laws. Current research investigates the generation of synthetic data as an efficient and privacy-ensuring alternative to collecting real data in the field. This survey introduces the basic definitions and methodologies, essential when generating and employing synthetic data for human analysis. We summarise current state-of-the-art methods and the main benefits of using synthetic data. We also provide an overview of publicly available synthetic datasets and generation models. Finally, we discuss limitations, as well as open research problems in this field. This survey is intended for researchers and practitioners in the field of human analysis.

Latent fingerprints are among the most important and widely used evidence in crime scenes, digital forensics and law enforcement worldwide. Despite the number of advancements reported in recent works, we note that significant open issues such as independent benchmarking and lack of large-scale evaluation databases for improving the algorithms are inadequately addressed. The available databases are mostly of semi-public nature, lack of acquisition in the wild environment, and post-processing pipelines. Moreover, they do not represent a realistic capture scenario similar to real crime scenes, to benchmark the robustness of the algorithms. Further, existing databases for latent fingerprint recognition do not have a large number of unique subjects/fingerprint instances or do not provide ground truth/reference fingerprint images to conduct a cross-comparison against the latent. In this paper, we introduce a new wild large-scale latent fingerprint database that includes five different acquisition scenarios: reference fingerprints from (1) optical and (2) capacitive sensors, (3) smartphone fingerprints, latent fingerprints captured from (4) wall surface, (5) Ipad surface, and (6) aluminium foil surface. The new database consists of 1,318 unique fingerprint instances captured in all above mentioned settings. A total of 2,636 reference fingerprints from optical and capacitive sensors, 1,318 fingerphotos from smartphones, and 9,224 latent fingerprints from each of the 132 subjects were provided in this work. The dataset is constructed considering various age groups, equal representations of genders and backgrounds. In addition, we provide an extensive set of analysis of various subset evaluations to highlight open challenges for future directions in latent fingerprint recognition research.

The development of large-scale identification systems that ensure the privacy protection of enrolled subjects represents a major challenge. Biometric deployments that provide interoperability and usability by including efficient multi-biometric solutions are a recent requirement. In the context of privacy protection, several template protection schemes have been proposed in the past. However, these schemes seem inadequate for indexing (workload reduction) in biometric identification systems. More specifically, they have been used in identification systems that perform exhaustive searches, leading to a degradation of computational efficiency. To overcome these limitations, we present an efficient privacy-preserving multi-biometric identification system that retrieves protected deep cancelable templates and is agnostic with respect to biometric characteristics and biometric template protection schemes. To this end, a multi-biometric binning scheme is designed to exploit the low intra-class variation properties contained in the frequent binary patterns extracted from different types of biometric characteristics. Experimental results reported on publicly available databases using state-of-the-art Deep Neural Network (DNN)-based embedding extractors show that the protected multi-biometric identification system can reduce the computational workload to approximately 57% (indexing up to three types of biometric characteristics) and 53% (indexing up to two types of biometric characteristics), while simultaneously improving the biometric performance of the baseline biometric system at the high-security thresholds.

The accuracy of face recognition skyrocketed in past years and its robustness towards various covariates has been shown, such as variations in pose or age. In recent years, a considerable amount of research efforts has been devoted to cross-domain face recognition aiming at comparing facial images obtained from domains that are different in capture technologies, e.g. visible spectrum versus infrared, or signal representations, e.g. photographs versus sketches. Yet, various relevant domains have hardly been explored and a lack of public databases hampers the development of new algorithms.In this work, we introduce the HDA Cross-Domain (HDA-CD) face image database comprising 1,400 face images from three different domains including avatars, caricatures, and sketches. Said face images were manually generated using popular mobile apps. In a benchmark, we evaluate commercial and open-source state-of-the-art facial analysis methods on the HDA-CD database including face detection and recognition. For the latter task, generated facial images are compared against their original counter-parts. The HDA-CD database is made publicly available at: https://dasec.h-da.de/hda-cdfdb/

Lossy face image compression can degrade the image quality and the utility for the purpose of face recognition. This work investigates the effect of lossy image compression on a state-of-the-art face recognition model, and on multiple face image quality assessment models. The analysis is conducted over a range of specific image target sizes. Four compression types are considered, namely JPEG, JPEG 2000, downscaled PNG, and notably the new JPEG XL format. Frontal color images from the ColorFERET database were used in a Region Of Interest (ROI) variant and a portrait variant. We primarily conclude that JPEG XL allows for superior mean and worst case face recognition performance especially at lower target sizes, below approximately 5kB for the ROI variant, while there appears to be no critical advantage among the compression types at higher target sizes. Quality assessments from modern models correlate well overall with the compression effect on face recognition performance.

Biometric data stored in automated recognition systems are at risk of attacks. This is particularly true for large-scale biometric identification systems, where the reference database is often accessed remotely. A popular approach for the protection of the stored templates is homomorphic encryption, which grants privacy protection while maintaining the biometric performance of the unprotected system. However, it introduces a significant computational overhead that can render identification transactions infeasible. To reduce this workload, biometric indexing in the encrypted domain has become a recent research interest. In this work, we show that in such schemes, auxiliary indexing data can leak additional privacy-sensitive information that violate standardized requirements for biometric template protection. In response to this leakage, we propose a novel framework HEBI that protects biometric indexing approaches at a post-quantum security level while requiring a computational effort of only 0.12 milliseconds per cluster.

Accurate face recognition systems are increasingly important in sensitive applications like border control or migration management. Therefore, it becomes crucial to quantify the quality of facial images to ensure that lowquality images are not affecting recognition accuracy. In this context, the current draft of ISO/IEC 29794-5 introduces the concept of component quality to estimate how single factors of variation affect recognition outcomes. In this study, we propose a quality measure (NeutrEx) based on the accumulated distances of a 3D face reconstruction to a neutral expression anchor. Our evaluations demonstrate the superiority of our proposed method compared to baseline approaches obtained by training Support Vector Machines on face embeddings extracted from a pre-trained Convolutional Neural Network for facial expression classification. Furthermore, we highlight the explainable nature of our NeutrEx measures by computing per-vertex distances to unveil the most impactful face regions and allow operators to give actionable feedback to subjects1.

Today, face recognition systems (FRS) are widely used in applications such as forensic and border control systems. Despite the increasing ability of deep neural networks to identify individuals based on their facial images, it remains challenging to recognize faces with long age gaps between reference and probe samples. To improve the robustness of FRS towards aging, training datasets can be enriched with synthetic data by simulating recurring aging effects. Typical aging signs include craniofacial changes during the child-to-adult age transition or textural changes that occur during adult-to-adult aging (e.g., wrinkles or furrows). Building upon the recent achievements of 3D-aware generative adversarial networks, we propose a geometry-aware face age modification algorithm (Age-EG3D) that enables lifespan face age simulation. We demonstrate the effectiveness of our approach by providing a comprehensive performance evaluation and comparison of Age-EG3D to prior works. All code and pre-trained models are available at https://github.com/johndoe133/eg3d-age.

Contactless fingerprint recognition is an emerging biometric technology and Presentation Attack Detection (PAD) methods are crucial to preserve system security. Convolutional Neural Networks (CNNs) represent the state-of the-art of PAD algorithms for many contactless captured biometric characteristics and various research groups proposed specialized CNN-based PAD methods or used general purpose CNNs to detect Presentation Attacks (PAs). In this work, we compare nine CNN-based PAD methods for contactless fingerprint PAD: five general purpose algorithms, and four dedicated PAD methods designed for various biometric characteristics. To achieve this, we combine the COLFISPOOF database with three bona fide databases: the HDA database and both versions of the ISPFD database. We set up our experiments using a baseline evaluation protocol and four Leave-One-Out (LOO) protocols, to benchmark the generalization capabilities to unseen data. The results reported by using the Attack Presentation Classification Error Rate (APCER) vs. Bona fide Presentation Classification Error Rate (BPCER) and the Detection Equal Error Rate (D-EER). Further, we discuss the achieved results in detail and give recommendations for real-world implementations. Our results show that established PAD algorithms for other biometric characteristics can accurately detect PAs on contactless fingerprints. While strong deviations between the considered PAD algorithms are observed, the best performing method shows a D-EER between 0.01% and 0.08% (depending on the LOO partition) and a APCER of 0.00% at a BPCER of 1.00%.

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.

Currently, it is ever more common to access online services for activities which formerly required physical attendance. From banking operations to visa applications, a significant number of processes have been digitised, especially since the advent of the COVID-19 pandemic, requiring remote biometric authentication of the user. On the downside, some subjects intend to interfere with the normal operation of remote systems for personal profit by using fake identity documents, such as passports and ID cards. Deep learning solutions to detect such frauds have been presented in the literature. However, due to privacy concerns and the sensitive nature of personal identity documents, developing a dataset with the necessary number of examples for training deep neural networks is challenging. This work explores three methods for synthetically generating ID card images to increase the amount of data while training fraud-detection networks. These methods include computer vision algorithms and Generative Adversarial Networks. Our results indicate that databases can be supplemented with synthetic images without any loss in performance for the print/scan Presentation Attack Instrument Species (PAIS) and a loss in performance of 1% for the screen capture PAIS.

The efficiency of biometric systems, in particular efficient and accurate biometric identification, is one of the most challenging open problems in biometrics today. Adding to that, biometric data are sensitive data deserving adequate protection. As a solution, this work proposes an efficient privacy-preserving reduction of the computational workload of biometric identification systems using public-key encryption with keyword search (PEKS). For long-term protection of the biometric data, fully homomorphic encryption is applied for template protection. As all applied cryptographic schemes are lattice-based, they also offer post-quantum security. Throughout the system, the recognition accuracy of the unprotected system is preserved. In an evaluation on a public face database, the computational workload of an identification search in the encrypted domain is reduced down to 8.4% compared to an exhaustive search, achieving identification on 1062 subjects in 210 milliseconds. Based on these results, an identification search on 1 million subjects can be estimated at under 3 minutes using off-the-shelf hardware.

Synthetic fingerprint image generation is important for the development of biometric recognition systems at scale due to the lack of easy-to-distribute real data, e.g. due to legal restrictions. Based on a SFinGe ridge line pat-tern, the recently introduced SynCoLFinGer algorithm gen-erates synthetic contactless fingerprints. However, it neglects some of the most frequent imperfections shown in common fingerprint databases. This work aims to analyse the most frequently found imperfections on fingerprint images and implements additional imperfections to SynCoLFinGer, including ink stains, dermatological issues, wounds and scars. The resem-blance of the generated fingerprints is assessed with re-gard to their sample quality and visually in comparison to real samples. Moreover, we report the biometric performance and sample quality on a generated database. The source code of this work is made publicly available under: https://gitlab.com/jannispriesnitz/syncolfinger

Homomorphic Encryption (HE) has become a well-known tool for privacy-preserving recognition in biometric systems. Despite some important advantages of HE (such as preservation of recognition accuracy), there are two main drawbacks in the application of HE to biometric recognition systems: first, the security of the system solely depends on the secrecy of the private (decryption) key; second, the computational costs of the operations on the ciphertexts are expensive. To address these challenges, in this paper we propose a hybrid scheme for the protection of biometric templates, which combines cancelable biometrics (CB) methods and HE. Applying CB prior to HE enhances both the security and privacy of the overall system, since the protected templates remain irreversible even if the secret keys are leaked (commonly referred to as the full disclosure scenario). In addition, we can reduce the dimensionality of templates using CB before applying HE, which speeds up the computation over the ciphertexts. We use BioHashing, Multi-Layer Perceptron (MLP) hashing, and Index-of-Maximum (IoM) hashing as different CB methods, and for each of these schemes, we propose a method for computing scores between hybrid-protected templates in the encrypted domain. We evaluate our proposed hybrid scheme using different state-of-the-art face recognition models (Ar-cFace, ElasticFace, and FaceNet) on the MOBIO and LFW datasets. The source code of our experiments is publicly available, so our work can be fully reproduced.

In this work, we present a simple biometric indexing scheme which is binning and retrieving cancelable deep face templates based on frequent binary patterns. The simplicity of the proposed approach makes it applicable to unprotected as well as protected, i.e. cancelable, deep face templates. As such, this approach represents to the best of the authors' knowledge the first generic indexing scheme that can be applied to arbitrary cancelable face templates (o binary representation). In experiments, deep face templates are obtained from the Labelled Faces in the Wild (LFW) dataset using the ArcFace face recognition system for feature extraction. Protected templates are then generated by employing different cancelable biometric schemes, i.e. BioHashing and two variants of Index-of-Maximum Hashing. The proposed indexing scheme is evaluated on closed- and open-set identification scenarios. It is shown to maintain the recognition accuracy of the baseline system while reducing the penetration rate and hence the workload of identifications to approximately 40%.

We investigate the potential of fusing human examiner decisions for the task of digital face manipulation detection. To this end, various decision fusion methods are proposed incorporating the examiners’ decision confidence, experience level, and their time to take a decision. Conducted experiments are based on a psychophysical evaluation of digital face image manipulation detection capabilities of humans in which different manipulation techniques were applied, i.e. face morphing, face swapping and retouching. The decisions of 223 participants were fused to simulate crowds of up to seven human examiners. Experimental results reveal that (1) despite the moderate detection performance achieved by single human examiners, a high accuracy can be obtained through decision fusion and (2) a weighted fusion, which takes the examiners’ decision confidence into account, yields the most competitive detection performance.

This research proposes a method to detect alcohol consumption from a Near-Infra-Red (NIR) periocular eye images. The study focuses on determining the effect of external factors such as alcohol on the Central Nervous System (CNS). The goal is to analyse how this impacts on iris and pupil movements and if it is possible to capture these changes with a standard iris NIR camera. This paper proposes a novel Fused Capsule Network (F-CapsNet) to classify iris NIR images taken under alcohol consumption subjects. The results show the F-CapsNet algorithm can detect alcohol consumption in iris NIR images with an accuracy of 92.3% using half of parameters than the standard Capsule Network algorithm. This work is a step forward for developing an automatic system to estimate "Fitness for Duty" and prevent accidents due to alcohol consumption.

Face recognition systems (FRS) are vulnerable to different kinds of attacks. Morphing attack combines multiple face images to obtain a single face image that can verify equally against all contributing subjects. Various Morphing Attack Detection (MAD) algorithms have been proposed in recent years albeit limited generalizability. We present a new approach for MAD in this work with better generalization than state-of-the-art (SOTA) algorithms. We propose an end-to-end multi-stage encoder-decoder network for learning the residuals of morphing process to detect attacks. Leveraging the residuals, we learn an efficient classifier using cross-entropy loss and asymmetric loss. The use of asymmetric loss in our approach is motivated by imbalanced distribution of morphs and bona fides. An extensive set of experiments are conducted on five different datasets consisting of two landmark based and three Generative Adversarial Network (GAN) based morphs in various settings such as digital, print-scan and print-scan-compression. We first demonstrate a near-ideal performance of the proposed MAD with Detection Equal Error Rate (D-EER) of 0% in the best case and 2.58% in the worst case in the digital domain in closed-set protocol, i.e., known attacks. Further, we demonstrate the applicability of the proposed approach on 60 different combinations where the testing set contains unknown morphing attacks in open-set protocol to illustrate the generalization ability of our proposed approach. Through training the proposed approach on landmark-based morph generation data alone, we obtain an EER of 3.59% in the best case and 12.89% in the worst case for morphed images in the digital domain, reducing the error rates from 45.67% and 30.23% respectively, in open-set protocol. We further present an extensive analysis of the proposed approach through Class Activation Maps (CAM) to explain the decisions using by making use of three complementary CAM analysis.

We present the first method for synthetic generation of contactless fingerprint images, referred to as SynCoLFinGer. To this end, the constituent components of contactless fingerprint images regarding capturing, subject characteristics, and environmental influences are modeled and applied to a synthetically generated ridge pattern using the SFinGe algorithm. The proposed method is able to generate different synthetic samples corresponding to a single finger and it can be parameterized to generate contactless fingerprint images of various quality levels. The resemblance of the synthetically generated contactless fingerprints to real fingerprints is confirmed by evaluating biometric sample quality using an adapted NFIQ 2.0 algorithm and biometric utility using a state-of-the-art contactless fingerprint recognition system.

Iris pattern recognition has significantly improved the biometric authentication field due to its high stability and uniqueness. Such physical characteristics have played an essential role in security applications and other related areas. However, presentation attacks, also known as spoofing techniques, can bypass biometric authentication systems using artefacts such as printed images, artificial eyes, textured contact lenses, etc. Many liveness detection methods that improve the robustness of these systems have been proposed. The first International Iris Liveness Detection competition, where the effectiveness of liveness detection methods is evaluated, was first launched in 2013, and its latest iteration was held in 2020. In this paper, we present the approach that won the LivDet-Iris 2020 competition using two-class scenarios (bona fide iris images vs. presentation attack iris images). Additionally, we propose new three-class and four-class scenarios that complement the competition results. These methods use a serial architecture based on a MobileNetV2 modification, trained from scratch to classify bona fide iris images versus presentation attack images. The bona fide class consists of live iris images, whereas the attack presentation instrument classes consist of cadaver, printed, and contact lenses images, for a total of four species. All the images were pre-processed and weighted per class to present a fair evaluation. This approach is primarily focused on detecting the bona fide class over improving the detection of presentation attack instruments. For the two, three, and four classes scenarios BPCER10values of 0.99%, 0.16%, and 0.83% were obtained respectively, whereas for the BPCER20values of 3.09%, 0.16%, and 3.77% were obtained, with the best model overall being the proposed 3-class serial model. This work reaches competitive results according to the reported results in the LivDet-Iris 2020 competition.

As face presentation attacks (PAs) are realistic threats for unattended face verification systems, face presentation attack detection (PAD) has been intensively investigated in past years, and the recent advances in face PAD have significantly reduced the success rate of such attacks. In this article, an empirical study on a novel and effective face impostor PA is made. In the proposed PA, a facial artifact is created by using the most vulnerable facial components, which are optimally selected based on the vulnerability analysis of different facial components to impostor PAs. An attacker can launch a face PA by presenting a facial artifact on his or her own real face. With a collected PA database containing various types of artifacts and presentation attack instruments (PAIs), the experimental results and analysis show that the proposed PA poses a more serious threat to face verification and PAD systems compared with the print, replay, and mask PAs. Moreover, the generalization ability of the proposed PA and the vulnerability analysis with regard to commercial systems are also investigated by evaluating unknown face verification and real-world PAD systems. It provides a new paradigm for the study of face PAs.

Recently, the use of remote education via electronic media, i.e. eLearning, has increased due to the COVID-19 pandemic. To achieve secure and reliable identity verification in eLearning exams, it has been suggested to employ face recognition technologies for remote student authentication in online examinations. However, novel attacks on face recognition in eLearning systems have rarely been considered in the scientific literature.In this work, we investigate the feasibility of so-called face morphing attacks in eLearning systems. Such attacks can be launched in scenarios where students are authenticated via identity documents containing face images that are remotely presented prior to online examinations. In this relevant scenario, students are able to fool human examination and automated face recognition by morphing their face image with that of an accomplice, e.g. fellow student. Resulting morphed face images contain biometric information of both subjects contributing to it. Consequentially, an accomplice could take part in an online examination for a student with high probability of passing an identity verification unnoticed. We assess the vulnerability of a commercial and an open-source face recognition system to said attack. To this end, a realistic dataset of morphing attacks is collected. It is shown that automated face recognition in eLearning systems can be tricked with alarmingly high success chance.

Recently, deep convolutional neural networks have achieved remarkable performance for the task of Single Image Super-Resolution (SISR); however these models set huge amount of computational complexity to achieve such performance. Hence, computationally efficient and low memory models are needed for SISR if such models are deployed on resources with low-computational devices (for instance, Mobile). We propose a novel approach referred to as Channel Split Convolutional Neural Network for Single Image Super-Resolution (CSISR). The proposed work aims to create a light-weight but efficient network to enhance SR performance. It employs a unique channel splitting alongside channel reduction blocks, leading to more effectiveness with a less computational burden to obtain state-of-the-art accuracy. Further, we suggest a new strategy for Channel Attention (CA) using a combination of global average and standard deviation pooling accompanied by conventional non-linear mapping layers to improve the learning. The performance of the proposed network is validated on various benchmark testing datasets for the SISR task, which shows its superiority over other existing methods.

For iris recognition in non-cooperative environments, iris segmentation has been regarded as the first most important challenge still open to the biometric community, affecting all downstream tasks from normalization to recognition. In recent years, deep learning technologies have gained significant popularity among various computer vision tasks and also been introduced in iris biometrics, especially iris segmentation. To investigate recent developments and attract more interest of researchers in the iris segmentation method, we organized the 2021 NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization (NIR-ISL 2021) at the 2021 International Joint Conference on Biometrics (IJCB 2021). The challenge was used as a public platform to assess the performance of iris segmentation and localization methods on Asian and African NIR iris images captured in non-cooperative environments. The three best-performing entries achieved solid and satisfactory iris segmentation and localization results in most cases, and their code and models have been made publicly available for reproducibility research.

Face recognition can benefit from the utilization of depth data captured using low-cost cameras, in particular for presentation attack detection purposes. Depth video output from these capture devices can however contain defects such as holes or general depth inaccuracies. This work proposes a deep learning face depth enhancement method in this context of facial biometrics, which adds a security aspect to the topic. U-Net-like architectures are utilized, and the networks are compared against hand-crafted enhancer types, as well as a similar depth enhancer network from related work trained for an adjacent application scenario. All tested enhancer types exclusively use depth data as input, which differs from methods that enhance depth based on additional input data such as visible light color images. Synthetic face depth ground truth images and degraded forms thereof are created with help of PRNet, to train multiple deep learning enhancer models with different network sizes and training configurations. Evaluations are carried out on the synthetic data, on Kinect v1 images from the KinectFaceDB, and on in-house RealSense D435 images. These evaluations include an assessment of the falsification for occluded face depth input, which is relevant to biometric security. The proposed deep learning enhancers yield noticeably better results than the tested preexisting enhancers, without overly falsifying depth data when non-face input is provided, and are shown to reduce the error of a simple landmark-based PAD method.

Morphing attacks have posed a severe threat to Face Recognition System (FRS). Despite the number of advancements reported in recent works, we note serious open issues such as independent benchmarking, generalizability challenges and considerations to age, gender, ethnicity that are inadequately addressed. Morphing Attack Detection (MAD) algorithms often are prone to generalization challenges as they are database dependent. The existing databases, mostly of semi-public nature, lack in diversity in terms of ethnicity, various morphing process and post-processing pipelines. Further, they do not reflect a realistic operational scenario for Automated Border Control (ABC) and do not provide a basis to test MAD on unseen data, in order to benchmark the robustness of algorithms. In this work, we present a new sequestered dataset for facilitating the advancements of MAD where the algorithms can be tested on unseen data in an effort to better generalize. The newly constructed dataset consists of facial images from 150 subjects from various ethnicities, age-groups and both genders. In order to challenge the existing MAD algorithms, the morphed images are with careful subject pre-selection created from the contributing images, and further post-processed to remove morphing artifacts. The images are also printed and scanned to remove all digital cues and to simulate a realistic challenge for MAD algorithms. Further, we present a new online evaluation platform to test algorithms on sequestered data. With the platform we can benchmark the morph detection performance and study the generalization ability. This work also presents a detailed analysis on various subsets of sequestered data and outlines open challenges for future directions in MAD research.

The deep learning models for the Single Image Super-Resolution (SISR) task have found success in recent years. However, one of the prime limitations of existing deep learning-based SISR approaches is that they need supervised training. Specifically, the Low-Resolution (LR) images are obtained through known degradation (for instance, bicubic downsampling) from the High-Resolution (HR) images to provide supervised data as an LR-HR pair. Such training results in a domain shift of learnt models when real-world data is provided with multiple degradation factors not present in the training set. To address this challenge, we propose an unsupervised approach for the SISR task using Generative Adversarial Network (GAN), which we refer to hereafter as DUS-GAN. The novel design of the proposed method accomplishes the SR task without degradation estimation of real-world LR data. In addition, a new human perception-based quality assessment loss, i.e., Mean Opinion Score (MOS), has also been introduced to boost the perceptual quality of SR results. The pertinence of the proposed method is validated with numerous experiments on different reference-based (i.e., NTIRE Real-world SR Challenge validation dataset) and no-reference based (i.e., NTIRE Real-world SR Challenge Track-1 and Track-2) testing datasets. The experimental analysis demonstrates committed improvement from the proposed method over the other state-of-the-art unsupervised SR approaches, both in terms of subjective and quantitative evaluations on different reference metrics (i.e., LPIPS, PI-RMSE graph) and no-reference quality measures such as NIQE, BRISQUE and PIQE. We also provide the implementation of the proposed approach (https://github.com/kalpeshjp89/DUSGAN) to support reproducible research.

Face morphing attacks have demonstrated a severe threat in the passport issuance protocol that weakens the border control operations. A morphed face images if used after printing and scanning (re-digitizing) to obtain a passport is very challenging to be detected as attack. In this paper, we present a novel method to detect such morphing attacks using an ensemble of features computed on the scale-space representation derived from the color space for a given image. Given the limited availability of datasets representing realistic morphing attacks, we introduce and present a new print-scan image dataset of morphed face images. Experiments are carried out on the two different datasets and compared with sixteen existing state-of-art Morphing Attack Detection (MAD) mechanism based on single image MAD (S-MAD). The proposed approach indicates a superior MAD performance on both datasets suggesting the applicability in operational scenarios.

Due to the wide operational deployment of biometric recognition systems, presentation attacks targeting the capture device have become a severe threat. Especially for fingerprint recognition, a high number of different materials allows the creation of numerous presentation attack instruments (PAIs) in the form of full fake fingers and fingerprint overlays, which very much resemble the skin properties at fingertips. As a consequence, automated presentation attack detection (PAD) mechanisms are of utmost importance. Utilising a 1310 nm laser in a new capture device, we present an evaluation of three long short-term memory (LSTM) networks in comparison to eight convolutional neural networks (CNNs) on a database comprising over 22,000 samples and including 45 different PAI species. The LSTMs analyse temporal properties within a captured sequence in order to detect blood movement, while the CNNs take into account spatial properties within a single frame to focus on reflections by the PAI material. The results show that the diversity of PAI species is too big for a single classifier to correctly detect all presentation attacks. However, by fusing the scores from distinct algorithms, we can achieve a detection accuracy of 3.71% APCER for a convenient BPCER of 0.2%.

ID cards are uniquely linked to one individual via a printed or electronically provided facial image. Even though the face is treated as universal and distinctive characteristic, twins can weaken this distinctiveness because of their biological similarity. Also, humans might falsely recognise an unknown person as a friend - colloquially named a Dop-pelgänger. Recently it was demonstrated that this biological effect of similar data subjects can be purposefully established between two individuals in order to improve the vulnerability of the so-called morphing attack. This image manipulation technique creates a melted facial image which is similar to two or more data subjects. If embedded into an ID card, the manipulated reference image can be used by all participating individuals and thus the concept of a unique link is broken. This work elaborates the rather neglected part of selecting morph pairs based on a similarity score instead of a simple random assignment. It discusses the applicability of different possible algorithms. The finally developed approach considers complex real-world constraints while being executable in a reasonable amount of time and producing acceptable large morph sets. It is shown that this algorithm greatly increases the vulnerability of automated face recognition systems. Surprisingly, it also proves that an effective pre-selection of pairs questions the need of in-depth optimized morphing algorithms.

Face morphing attacks have raised critical concerns as they demonstrate a new vulnerability of Face Recognition Systems (FRS), which are widely deployed in border control applications. The face morphing process uses the images from multiple data subjects and performs an image blending operation to generate a morphed image of high quality. The generated morphed image exhibits similar visual characteristics corresponding to the biometric characteristics of the data subjects that contributed to the composite image and thus making it difficult for both humans and FRS, to detect such attacks. In this paper, we report a systematic investigation on the vulnerability of the Commercial-Off- The-Shelf (COTS) FRS when morphed images under the influence of ageing are presented. To this extent, we have introduced a new morphed face dataset with ageing derived from the publicly available MORPH II face dataset, which we refer to as MorphAge dataset. The dataset has two bins based on age intervals, the first bin - MorphAge-I dataset has 1002 unique data subjects with the age variation of 1 year to 2 years while the MorphAge-II dataset consists of 516 data subjects whose age intervals are from 2 years to 5 years. To effectively evaluate the vulnerability for morphing attacks, we also introduce a new evaluation metric, namely the Fully Mated Morphed Presentation Match Rate (FMMPMR), to quantify the vulnerability effectively in a realistic scenario. Extensive experiments are carried out using two different COTS FRS (COTS I Cognitec FaceVACS-SDK Version 9.4.2 and COTS II - Neurotechnology version 10.0) to quantify the vulnerability with ageing. Further, we also evaluate five different Morph Attack Detection (MAD) techniques to benchmark their detection performance with respect to ageing.

The paper presents a summary of the 2020 Sclera Segmentation Benchmarking Competition (SSBC), the 7th in the series of group benchmarking efforts centred around the problem of sclera segmentation. Different from previous editions, the goal of SSBC 2020 was to evaluate the performance of sclera-segmentation models on images captured with mobile devices. The competition was used as a platform to assess the sensitivity of existing models to i) differences in mobile devices used for image capture and ii) changes in the ambient acquisition conditions. 26 research groups registered for SSBC 2020, out of which 13 took part in the final round and submitted a total of 16 segmentation models for scoring. These included a wide variety of deep-learning solutions as well as one approach based on standard image processing techniques. Experiments were conducted with three recent datasets. Most of the segmentation models achieved relatively consistent performance across images captured with different mobile devices (with slight differences across devices), but struggled most with low-quality images captured in challenging ambient conditions, i.e., in an indoor environment and with poor lighting.

In this study we analyzed longitudinal fingerprint data of 20 data subjects, acquired over a time span of up to 12 years. Using hierarchical linear modeling, we aimed to delineate mated similarity scores as a function of fingerprint quality and of the time interval between reference and probe images. Our results did not reveal effects on mated similarity scores caused by an increasing time interval across subjects, but rather individual effects on mated similarity scores. The results are in line with the general assumption that the fingerprint as a biometric characteristic and the features extracted from it do not change over the adult life span. However, it contradicts several related studies that reported noticeable template ageing effects. We discuss why different findings regarding ageing of references in fingerprint recognition systems were made.

Wrist-wearable devices such as smartwatch hardware have gained popularity as they provide quick access to various information and easy access to multiple applications. Among the numerous smartwatch applications, user verification based on the handwriting is gaining momentum by considering its reliability and user-friendliness. In this paper, we present a novel technique for user verification using a smartwatch based writing pattern or style. The proposed approach leverages accelerometer data captured from the smartwatch that is further represented using 2D Continuous Wavelet Transform (CWT) and deep features extracted using the pre-trained ResNet50. These features are classified using an ensemble of classifiers to make the final decision on user verification. Extensive experiments are carried out on a newly captured dataset using two different smartwatches with three different writing scenarios (or activities). Experimental results provide critical insights and analysis of the results in such a verification scenario.

Facial cosmetics have the ability to substantially alter the facial appearance, which can negatively affect the decisions of a face recognition. In addition, it was recently shown that the application of makeup can be abused to launch so-called makeup presentation attacks. In such attacks, the attacker might apply heavy makeup in order to achieve the facial appearance of a target subject for the purpose of impersonation. In this work, we assess the vulnerability of a COTS face recognition system to makeup presentation attacks employing the publicly available Makeup Induced Face Spoofing (MIFS) database. It is shown that makeup presentation attacks might seriously impact the security of the face recognition system. Further, we propose an attack detection scheme which distinguishes makeup presentation attacks from genuine authentication attempts by analysing differences in deep face representations obtained from potential makeup presentation attacks and corresponding target face images. The proposed detection system employs a machine learning-based classifier, which is trained with synthetically generated makeup presentation attacks utilizing a generative adversarial network for facial makeup transfer in conjunction with image warping. Experimental evaluations conducted using the MIFS database reveal a detection equal error rate of 0.7% for the task of separating genuine authentication attempts from makeup presentation attacks.

Along with the deployment of the Face Recognition Systems (FRS), concerns were raised related to the vulnerability of those systems towards various attacks including morphed attacks. The morphed face attack involves two different face images in order to obtain via a morphing process a resulting attack image, which is sufficiently similar to both contributing data subjects. The obtained morphed image can successfully be verified against both subjects visually (by a human expert) and by a commercial FRS. The face morphing attack poses a severe security risk to the e-passport issuance process and to applications like border control, unless such attacks are detected and mitigated. In this work, we propose a new method to reliably detect a morphed face attack using a newly designed demising framework. To this end, we design and introduce a new deep Multi-scale Context Aggregation Network (MS-CAN) to obtain denoised images, which is subsequently used to determine if an image is morphed or not. Extensive experiments are carried out on three different morphed face image datasets. The Morphing Attack Detection (MAD) performance of the proposed method is also benchmarked against 14 different state-of-the-art techniques using the ISO-IEC 30107-3 evaluation metrics. Based on the obtained quantitative results, the proposed method has indicated the best performance on all three datasets and also on cross-dataset experiments.

The periocular region is used for authentication in the recent days under unconstrained acquisition in biometrics. This work presents two new feature extraction techniques to achieve robust and blur invariant biometric verification using periocular images captured using smartphones - (1) Deep Sparse Features (DSF) and (2) Deep Sparse Time Frequency Features (DeSTiFF). Both the approaches are based on extracting features via convolution of periocular images with a set of filters also referred as Deep Sparse Filters. The filters are learnt using natural image patches and sparse filtering approach. The DSF is obtained through convolution via Deep Sparse Filters. Further, convoluted responses are analyzed using Short Term Fourier Transform (STFT) to obtain time and frequency features of the images referred as DeSTIFF. The features obtained from the newly proposed feature extraction techniques are further represented in a collaborative subspace to achieve better verification performance. Both of the proposed feature extraction schemes are evaluated on two publicly available smartphone periocular databases and a new database (Visible Spectrum Periocular Image (VISPI) database) released with this article. The robustness of the proposed feature extraction is exemplified by comparing it with state-of-art approaches along with multiple deep networks where the improvement is evidently seen on large scale database with an average verification accuracy of Genuine Match Rate ≈ 98% at False Match Rate = 0.01%. We further support reproducible research by making the code and the database available for the academic research.

The vulnerability of facial recognition systems to face morphing attacks is well known. Many different approaches for morphing attack detection (MAD) have been proposed in the scientific literature. However, the MAD algorithms proposed so far have mostly been trained and tested on datasets whose distributions of image characteristics are either very limited (e.g., only created with a single morphing tool) or rather unrealistic (e.g., no print-scan transformation). As a consequence, these methods easily overfit on certain image types and the results presented cannot be expected to apply to real-world scenarios. For example, the results of the latest NIST FRVT MORPH show that the majority of submitted MAD algorithms lacks robustness and performance when considering unseen and challenging datasets. In this work, subsets of the FERET and FRGCv2 face databases are used to create a realistic database for training and testing of MAD algorithms, containing a large number of ICAO-compliant bona fide facial images, corresponding unconstrained probe images, and morphed images created with four different face morphing tools. Furthermore, multiple post-processings are applied on the reference images, e.g., print-scan and JPEG2000 compression. On this database, previously proposed differential morphing algorithms are evaluated and compared. In addition, the application of deep face representations for differential MAD algorithms is investigated. It is shown that algorithms based on deep face representations can achieve very high detection performance (less than 3% D-EER) and robustness with respect to various post-processings. Finally, the limitations of the developed methods are analyzed.

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).

In recent years, morphing of facial images has arisen as an important attack vector on biometric systems. Detection of morphed images has proven challenging for automated systems and human experts alike. Likewise, in recent years, the importance of efficient (fast) biometric identification has been emphasised by the rapid rise and growth of large-scale bio-metric systems around the world.In this paper, the aforementioned, hitherto unrelated, topics within the biometrics domain are combined: the properties of morphed images are exploited for the purpose of improving the transaction times of a biometric identification system. Specifically, morphs of two or more samples are used in the pre-selection step of a two-stage biometric identification system. In a proof-of-concept experimental evaluation using two state-of-the-art open-source facial recognition frameworks it is shown, that the proposed system achieves hit rates comparable to that of an exhaustive search-based baseline, while significantly reducing the penetration rate (and thus the computational workload) associated with the biometric identification transactions.

Facilitation of fake face generation in recent years, thanks to advancements in computer graphics and artificial intelligence, raises concerns about malicious use of these techniques for personal or political gains. Media consumers are exposed to hours of audiovisual content daily, while their vulnerability to fake audiovisual content is not yet fully studied and understood. In contrast, many recent automated fake content generation techniques are readily accessible to the public. A first step to address this vulnerability is to study the effectiveness of existing methods in passing human judgment. To this end, we examined the performance of 30 participants in the detection of 48 real and fake videos. The fake videos were sourced from six different methods of generation and were collected from a public video sharing website1, ranging from prosthetic makeup to Deepfakes. Our results show that the participants failed to detect two different types of fake videos. However, participants' detection performance improves when they know of the displayed individual or when a biometric reference video (introducing the individual and its behavior) is available to them during the test.

Speech recordings are a rich source of personal, sensitive data that can be used to support a plethora of diverse applications, from health profiling to biometric recognition. It is therefore essential that speech recordings are adequately protected so that they cannot be misused. Such protection, in the form of privacy-preserving technologies, is required to ensure that: (i) the biometric profiles of a given individual (e.g., across different biometric service operators) are unlinkable; (ii) leaked, encrypted biometric information is irreversible, and that (iii) biometric references are renewable. Whereas many privacy-preserving technologies have been developed for other biometric characteristics, very few solutions have been proposed to protect privacy in the case of speech signals. Despite privacy preservation this is now being mandated by recent European and international data protection regulations. With the aim of fostering progress and collaboration between researchers in the speech, biometrics and applied cryptography communities, this survey article provides an introduction to the field, starting with a legal perspective on privacy preservation in the case of speech data. It then establishes the requirements for effective privacy preservation, reviews generic cryptography-based solutions, followed by specific techniques that are applicable to speaker characterisation (biometric applications) and speech characterisation (non-biometric applications). Glancing at non-biometrics, methods are presented to avoid function creep, preventing the exploitation of biometric information, e.g., to single out an identity in speech-assisted health care via speaker characterisation. In promoting harmonised research, the article also outlines common, empirical evaluation metrics for the assessment of privacy-preserving technologies for speech data.

The vulnerability of face recognition systems to attacks based on morphed biometric samples has been established in the recent past. Such attacks pose a severe security threat to a biometric recognition system in particular within the widely deployed border control applications. However, so far a reliable detection of morphed images has remained an unsolved research challenge. In this work, automated morph detection algorithms based on general purpose pattern recognition algorithms are benchmarked for two scenarios relevant in the context of fraud detection for electronic travel documents, i.e. single image (no-reference) and image pair (differential) morph detection. In the latter scenario a trusted live capture from an authentication attempt serves as additional source of information and, hence, the difference between features obtained from this face image and a potential morph can be estimated. A dataset of 2,206 ICAO compliant bona fide face images of the FRGCv2 face database is used to automatically generate 4,808 morphs. It is shown that in a differential scenario morph detectors which utilize a score level-based fusion of detection scores obtained from a single image and differences between image pairs generally outperform no-reference morph detectors with regard to the employed algorithms and used parameters. On average a relative improvement of more than 25% in terms of detection equal error rate is achieved.

Face recognition systems are widely known for their vulnerability against presentation attacks or spoofing attacks. The exponential deployment of face recognition systems has been further challenged even by the simple and low-cost face artefacts generated using conventional printers. In this paper, we present a novel scheme to detect face presentation attacks posed by high-quality print attacks which are relatively difficult to detect. The proposed scheme leverages the phase information extracted from the spatial-frequency representation of the given image. We also present a new face presentation attack database collected using the iPhone 6S. The new database is comprised of 100 subjects collected in two different sessions that have resulted in a total of 31228 samples (or images). Extensive experiments are carried out on the newly constructed database and the obtained results show the improved performance of the proposed scheme when compared aaainst six different state-of-the-art methods.

Multi-spectral imaging has been explored to handle a set of deficiencies found in traditional imaging that capture the images only in visible spectrum (VIS) or Near-Infra Red (NIR) spectrum. The promising performance obtained in the experimental works indicates the use-case in real-life biometric systems. As biometric systems should also consider protecting biometric templates, it is required to have an efficient template protection scheme for multi-spectral biometric systems to avoid the leakage of biometric data and subsequent linkability issues. In this work, we propose a new template protection scheme for multi-spectral biometric systems through the use of biometric information across different spectra to provide protected templates. Through the proposed approach of kernalized hashing, we can reach a fully unlinkable template protection scheme that works across all spectra in a multi-spectral system with a comparable performance to unprotected system. Further, we propose a template level fusion across all the spectral bands to improve the performance of the multi-spectral biometric system with integrated template protection. Through the use of a relatively large sized multispectral face biometric database of 168 subjects captured in 9 narrow spectral bands in VIS and NIR bands (530nm to 1000nm), we illustrate the effectiveness of the proposed approach in achieving a robust and secure template protection while accounting for irreversibility, unlinkability and renewability. Through the experiments we establish the performance of the proposed template protection approach and demonstrate a high Genuine Match Rate (GMR ≈ 100% at False Accept Rate of FMR=0.01%) and low Equal Error Rate EER= ≈ 0%, while satisfying other requirements of biometric template protection. Further, we present a security analysis of the proposed approach to demonstrate the unlinkability of the biometric templates.

Biometric systems are prone to random and systematic errors which are typically attributed to the variations in terms of inter-session data capture and intra-session variability. Furthermore, these errors cannot be defined and modeled mathematically in many cases, but we can associate them with uncertainty based on certain conditions. In such cases, one of the possible approach to improve biometric system performance is to employ multi-biometric fusion by incorporating the uncertainties. In the literature, researchers have proposed many fusion techniques, but most of these techniques do not take uncertainty into account while performing fusion. Since the decision made by uni-modal biometric comparators do not consider the uncertainty involved in such decisions, it is essential first to model the uncertainty before combining the decision from multiple uni-modal biometric systems efficiently. To this end, we propose a score level multi-biometric fusion scheme using Subjective Logic which incorporates the uncertainty of the system's information channels while fusing the scores. Extensive experiments are carried out on the multi-biometric NIST BSSR1, and the proposed scheme has indicated a superior performance with a genuine match rate of 99.02 % at a false match rate fixed to 0.01 %.

Feature vectors extracted from biometric characteristics are often represented using floating point values. It is, however, more appealing to store and compare feature vectors in a binary representation, since it generally requires less storage and facilitates efficient comparators which utilise intrinsic bit operations. Furthermore, the binary representations are very often necessary for some specific application scenarios, e.g. template protection and indexing. In recent years, usage of deep neural networks for facial recognition has vastly improved the biometric performance of said systems. In this paper, various binarisation schemes are applied to such feature vectors and benchmarked for biometric performance. It is shown that with only a negligible drop in biometric performance, the storage space and computational requirements can be vastly decreased.

Presentation attacks against Face Recognition System (FRS) have incrementally posed challenges to create new detection methods. Among the various presentation attacks, disguise attacks allow concealing the identity of the attacker thereby increasing the vulnerability of the FRS. In this paper, we present a new approach for attack detection in multi-spectral systems, where face disguise attacks are carried out. The approach is based on using spectral signatures obtained from a spectral camera operating in eight narrow spectral bands across the Visible (VIS) and Near Infra-Red (NIR) (530nm to 1000nm) spectrum and learning deeply coupled auto-encoders. The robustness of the proposed approach is validated using a newly collected spectral face database of subjects conducting both bona fide (i.e. real) presentations and disguise attack presentations. The database is designed to capture 2 different kinds of attacks from 54 subjects, amounting to a total number of 6480 samples. Extensive experiments carried on the multi-spectral face database indicate the robust performance of proposed scheme when benchmarked with three different state-of-the-art methods.

The wide deployment of biometric recognition systems in the last two decades has raised privacy concerns regarding the storage and use of biometric data. As a consequence, the ISO/IEC 24745 international standard on biometric information protection has established two main requirements for protecting biometric templates: irreversibility and unlinkability. Numerous efforts have been directed to the development and analysis of irreversible templates. However, there is still no systematic quantitative manner to analyze the unlinkability of such templates. In this paper, we address this shortcoming by proposing a new general framework for the evaluation of biometric templates' unlinkability. To illustrate the potential of the approach, it is applied to assess the unlinkability of the four state-of-the-art techniques for biometric template protection: biometric salting, bloom filters, homomorphic encryption, and block re-mapping. For the last technique, the proposed framework is compared with other existing metrics to show its advantages.

In contrast to electronic travel documents (e.g. ePassports), the standardisation of breeder documents (e.g. birth certificates), regarding harmonisation of content and contained security features is in statu nascendi. Due to the fact that breeder documents can be used as an evidence of identity and enable the application for electronic travel documents, they pose the weakest link in the identity life cycle and represent a security gap for identity management. In this work, we present a cost efficient way to enhance the long-term security of breeder documents by utilizing blockchain technology. A conceptual architecture to enhance breeder document long-term security and an introduction of the concept's constituting system components is presented. Our investigations provide evidence that the Bitcoin blockchain is most suitable for breeder document long-term security.

Multi-spectral imaging has recently acquired significant attention in biometrics based authentication due to it's potential ability to capture spatio-spectral images across the electromagnetic spectrum. Especially, in the case of facial biometrics, multi-spectral imaging has shown significant promising results under unknown/varying illumination environment. However, the challenge arises when surveillance cameras provide the visible images while the enrollment are spectral band images. In order to address the backward/cross compatibility of probing visible images from regular surveillance cameras against the high quality spectral band images in enrollment, development of robust algorithms are required. In this paper, we present a new approach of selecting optimal band based on highest correlation coefficients of individual feature vectors from bands in comparison with feature vectors from visible images of respective individual classes for robust recognition performance. The proposed approach of band selection is validated on a newly collected face database of 168 subjects whose face images are collected in 9 different spectral bands and correspondingly their visible images from a regular camera operating in visible spectrum. The extensive set of experiments conducted on the new database with selected single band and multiple spectral bands in enrollment data versus the visible probe image has indicated the significance of the band selection. The new approach of spectral to visible matching with the proposed band selection method shows significant Rank- 1 recognition rate of 94.04% supporting the applicability of proposed method

Multispectral face recognition systems are widely used in various access control applications. The vulnerability of multispectral face recognition sensors towards low-cost Presentation Attack Instrument (PAI) such as printed photos used in attacks has emerged as a serious security threat. In this paper, we present a novel framework to detect presentation attacks against an extended multispectral face sensor. The proposed framework stems from the idea of exploring the complementary information available from different bands of an extended multispectral face sensor. To this extent, two different frameworks are proposed where the first framework is based on image fusion and the second builds on the Presentation Attack Detection (PAD) score level fusion. Extensive experiments are carried out on the extended multispectral face sensor database comprising of 50 subjects with two different presentation attacks generated using the printed photo artefacts. The obtained results indicate the superior performance of the PAD score level fusion on detecting both known and unknown attacks.

Periocular characteristics has gained substantial importance in recent times to supplement the performance of facial biometrics or as a stand-alone characteristics. While most of the current biometric systems for authentication or surveillance operate either in NIR spectrum or visible spectrum, the ocular information can be well utilized if a comparison of images from different spectra has to be conducted. In this work, we present a novel approach employing the features obtained from steerable pyramids to compare the ocular images captured from NIR versus the images captured from visible spectrum. The set of features obtained using the proposed cross-spectral approach are then used to learn a multi-class SVM classifier such that the probe image originating from another spectrum can be classified. Further, a fusion frame-work for combining the scores from different orientations of the steerable pyramid is proposed for a particular scale to strengthen the biometric performance of the algorithm. An extensive set of experiments conducted on a large database consisting of ocular images captured from 120 subjects (240 unique ocular instances) indicates the robustness of the proposed approach with a GMR of 100% at the FMR of 0.01% in a benchmark against other state-of-the-art techniques suggesting the applicability of proposed approach to greater extent.

With the availability of sensor technology across the broad electromagnetic spectrum, multi-spectral imaging is increasingly used in biometric systems. Especially for face recognition, multi-spectral imaging has gained a lot of attention due to it's invariant property against variation caused by unknown illumination. However, obtaining best performance using multi-spectral imaging is still a challenge due to presence of a modality gap between the spectral imaging data and redundant band information. In this paper, we propose a fused band representation with a set of selected bands represented in Quaternion space for spectral band images to efficiently maintain the inter band relationship in spatial domain. The selection is based on measuring the information content in bands using entropy and fusion is carried out in Quaternion space for three best bands. The features from newly obtained image is collaboratively represented to achieve robust performance. The proposed approach is experimentally validated on the extended multi-spectral face database of 168 subjects, whose spectral band images are captured in 9 narrow spectral bands in visible and near infrared range (530nm to 1000nm). The quantitative performance analysis, obtained using the proposed method indicates 96.13% recognition rate at Rank-1, outperforming other state-of-the-art methods.

We present a multi-iris indexing system for efficient and accurate large-scale identification. The system is based on Bloom filters and binary search trees. We describe and empirically evaluate several possible information fusion strategies for the system. Those experiments are performed using a combination of several publicly available datasets; the proposed system is tested in an open-set identification scenario consisting of 6,000 genuine and 100,000 impostor transactions. The system maintains the near-optimal biometric performance of an iris-code, score fusion based baseline system, while reducing the required lookup workload to less than 1% thereof.

The necessity of biometric template alignment imposes a significant computational load and increases the probability of false positive occurrences in biometric systems. While for some modalities, automatic pre-alignment of biometric samples is utilised, this topic has not yet been explored for systems based on the iris. This paper presents a method for pre-alignment of iris images based on the positions ofautomatically detected eye corners. Existing work in the area of automatic eye corner detection has hitherto only involved visible wavelength images; for the near-infrared images, used in the vast majority of current iris recognition systems, this task is significantly more challenging and as of yet unexplored. A comparative study of two methods for solving this problem is presented in this paper. The eye corners detected by the two methods are then used for the pre-alignment and biometric performance evaluation experiments. The system utilising image pre-alignment is benchmarked against a baseline iris recognition system on the iris subset of the BioSecure database. In the benchmark, the workload associated with alignment compensation is significantly reduced, while the biometric performance remains unchanged or even improves slightly.

The Face Recognition System (FRS) is known to be vulnerable to the attacks using the morphed face. As the use of face characteristics are mandatory in the electronic passport (ePass), morphing attacks have raised the potential concerns in the border security. In this paper, we analyze the vulnerability of the FRS to the new attack performed using the averaged face. The averaged face is generated by simple pixel level averaging of two face images corresponding to two different subjects. We benchmark the vulnerability of the commercial FRS to both conventional morphing and averaging based face attacks. We further propose a novel algorithm based on the collaborative representation of the micro-texture features that are extracted from the colour space to reliably detect both morphed and averaged face attacks on the FRS. Extensive experiments are carried out on the newly constructed morphed and averaged face image database with 163 subjects. The database is built by considering the real-life scenario of the passport issuance that typically accepts the printed passport photo from the applicant that is further scanned and stored in the ePass. Thus, the newly constructed database is built to have the print-scanned bonafide, morphed and averaged face samples. The obtained results have demonstrated the improved performance of the proposed scheme on print-scanned morphed and averaged face database.

Smartphone based facial biometric systems have been well used in many of the security applications starting from simple phone unlocking to secure banking applications. This work presents a new approach of exploring the intrinsic characteristics of the smartphone camera to capture a number of stack images in the depth-of-field. With the set of stack images obtained, we present a new feature-free and classifier-free approach to provide the presentation attack resistant face biometric system. With the entire system implemented on the smartphone, we demonstrate the applicability of the proposed scheme in obtaining a stack of images with varying focus to effectively determine the presentation attacks. We create a new database of 13250 images at different focal length to present a detailed analysis of vulnerability together with the evaluation of proposed scheme. An extensive evaluation of the newly created database comprising of 5 different Presentation Attack Instruments (PAI) has demonstrated an outstanding performance on all 5 PAI through proposed approach. With the set ofcomplementary benefits of proposed approach illustrated in this work, we deduce the robustness towards unseen 2D attacks.

Morphing techniques can be used to create artificial biometric samples, which resemble the biometric information of two (or more) individuals in image and feature domain. If morphed biometric images or templates are infiltrated to a biometric recognition system the subjects contributing to the morphed image will both (or all) be successfully verified against a single enrolled template. Hence, the unique link between individuals and their biometric reference data is annulled. The vulnerability of face and fingerprint recognition systems to such morphing attacks has been assessed in the recent past. In this paper we investigate the feasibility of morphing iris-codes. Two relevant attack scenarios are discussed and a scheme for morphing pairs of iris-codes depending on the expected stability of their bits is proposed. Different iris recognition systems, which accept comparison scores at a recommended Hamming distance of 0.32, are shown to be vulnerable to attacks based on the presented morphing technique.

Estimation of the orientation field is one of the key challenges during biometric feature extraction from a fingerprint sample. Many important processing steps rely on an accurate and reliable estimation. This is especially challenging for samples of low quality, for which in turn accurate preprocessing is essential. Regressional Convolutional Neural Networks have shown their superiority for bad quality samples in the independent benchmark framework FVC-ongoing. This work proposes to incorporate Deep Expectation. Options for further improvements are evaluated in this challenging environment of low quality images and small amount of training data. The findings from the results improve the new algorithm called DEX-OF. Incorporating Deep Expectation, improved regularization, and slight model changes DEX-OF achieves an RMSE of 7.52° on the bad quality dataset and 4.89° at the good quality dataset at FVC-ongoing. These are the best reported error rates so far.

This work presents the 2ndCross-Spectrum Iris/Periocular Recognition Competition (Cross-Eyed2017). The main goal of the competition is to promote and evaluate advances in cross-spectrum iris and periocular recognition. This second edition registered an increase in the participation numbers ranging from academia to industry: five teams submitted twelve methods for the periocular task and five for the iris task. The benchmark dataset is an enlarged version of the dual-spectrum database containing both iris and periocular images synchronously captured from a distance and within a realistic indoor environment. The evaluation was performed on an undisclosed test-set. Methodology, tested algorithms, and obtained results are reported in this paper identifying the remaining challenges in path forward.

18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017, Stockholm, Sweden, 20-24 August 2017

Spectral face recognition is gaining importance as the information from different bands can lead to robust face representation that are presentation (a.k.a, spoofing) attack resistant. However, the key challenge here is to process the high dimensional spatio-spectral data to extract and represent the reliable data while discarding redundant data information for processing. In this work, we present a new approach to represent the spectral images in a holistic manner by employing the well-known Projection Metric Learning (PML) in Grassmann manifold such that the redundant information from the spectral data is discarded while retaining significant information. The approach is adopted to learn discriminative information from an high dimensional spectral dataset. Further, we propose an extension using collaborative representation of learnt projection metrics for improving the classification accuracy of spectral data. With the extensive set of experiments conducted on a relatively large scale extended-spectral face image database (6048 images) of 168 subjects, we demonstrate the applicability of the proposed framework. The obtained results indicates highest accuracy by achieving a Rank-1 recognition rate of 98.21% in classifying the spectral face images.

Gender prediction based on facial features has received significant attention in computer vision and biometric community. Most of the gender classification studies mainly focused there attention on approaches that operate in the visible spectrum. In this paper we present gender classification using extended multi-spectral face data captured in nine narrow spectral bands across the visible near infrared spectrum (530nm to 1000nm). Further, we present the proposed method, that learns for this image set the discriminative spectral band features in the affine space and then classifies the features with a Support Vector Machine (SVM) in a robust manner. The extensive experimental results are presented on the reasonable sample size of 78300 spectral band images using our proposed method. The obtained results shows 90.49±3.56% average classification accuracy, indicating the applicability of our proposed method for gender classification.

Contact lens detection in the eye is a significant task to improve the reliability of iris recognition systems. A contact lens overlays the iris region and prevents the iris sensor from capturing the normal iris region. In this paper, we present a novel scheme for detection to detecting a contact lens using Deep Convolutional Neural Network (CNN). The proposed CNN architecture ContlensNet is structured to have fifteen layers and configured for the three-class detection problem with the following classes: images with textured (or colored) contact lens, soft (or transparent) contact lens, and no contact lens. The proposed ContlensNet is trained using numerous iris image patches and the problem of overfitting the network is addressed by using the dropout regularization method. Extensive experiments are carried out on two publicly available large-scale databases, namely: IIIT-Delhi Contact lens iris database (IIITD) and Notre Dame cosmetic contact lens database 2013 (ND) that are comprised of contact lens iris samples captured using four different sensors. The obtained results have demonstrated the improved performance of the proposed scheme with an average performance improvement of more than 10% in Correct Classification Rate (CCR%) when compared with eight different state-of-the-art contact lens detection systems.

Performance estimation is crucial to the assessment of novel algorithms and systems. In detection error tradeoff (DET) diagrams, discrimination performance is solely assessed targeting one application, where cross-application performance considers risks resulting from decisions, depending on application constraints. For the purpose of interchangeability of research results across different application constraints, we propose to augment DET curves by depicting systems regarding their support of security and convenience levels. Therefore, application policies are aggregated into levels based on verbal likelihood ratio scales, providing an easy to use concept for business-to-business communication to denote operative thresholds. We supply a reference implementation in Python, an exemplary performance assessment on synthetic score distributions, and a fine-tuning scheme for Bayes decision thresholds, when decision policies are bounded rather than fix.

The vast majority of text-independent speaker recognition systems rely on intermediate-sized vectors (i-vectors), which are compared by probabilistic linear discriminant analysis (PLDA). This paper proposes a PLDA-alike approach with restricted Boltzmann machines for i-vector based speaker recognition: two deep architectures are presented and examined, which aim at suppressing channel effects and recovering speaker-discriminative information on back-ends trained on a small dataset. Experiments are carried out on the MOBIO SRE'13 database, which is a challenging and publicly available dataset for mobile speaker recognition with limited amounts of training data. The experiments show that the proposed system outperforms the baseline i-vector/PLDA approach by relative gains of 31% on female and 9% on male speakers in terms of half total error rate.

Smartphone based periocular recognition has received substantial attention from the biometric research community. In this work, we propose a new scheme for the smartphone based periocular recognition. The proposed scheme is based on the texture features extracted from the periocular images using Maximum Response (MR) filters. These texture features are then classified using a deep neural network based on deeply coupled autoencoders. Extensive experiments are carried out on the large-scale VISOB database with 550 subjects captured using three different smartphones. The obtained results demonstrate the average performance of the proposed scheme with GMR of over 92% at FMR = 10-3.

Ocular recognition on smartphone authentication applications are gaining popularity in academic research and in the commercial sector where operators are requesting reliable and robust biometric authentication. The wide acceptance of such ocular based authentication systems also depends on the verification performance on large scale testing with different data subject ethnic groups and platforms. In this work, we evaluate such a large database of ocular images collected using three different phones. Further, we benchmark the verification performance and propose a new framework to improve it. The proposed framework is based on collaboratively represented features from deep sparse filtering. We obtain a verification performance of Genuine Match Rate (GMR) of 97.56% at a False Match Rate (FMR) of 0.001% for periocular images obtained from a Samsung device. The overall performance of around 95% GMR at a FMR of 0.001% not only indicates the robust nature of the proposed framework, but also illustrates the efficacy in applying them for real-life authentication scenarios.

Drug abuse leads to high degree of the change in the facial structure of a person due to multiple factors which include loss of fat in face, change of facial structure due to changes in facial muscles and change of skin texture due to appearance of acnes. The combination of such effects lead to completely deformed face which presents a complex challenge to face based authentication systems. To study the impact of the drug-abuse on face recognition systems, in this work, we create a new face database collected before and after the drug-abuse. The newly collected database which is referred as Drug Abuse Database (DAD) consists of images obtained from 101 subjects. Further, the collected database is analysed using various state-of-art face recognition algorithms along with a widely used commercial-off-the-shelf (COTS) system. The obtained performance of GMR = 12.90% at FMR = 0.01% indicates the challenge presented by such face images and underlines the importance of newer algorithms to handle such challenge.

The challenges of presentation attacks (spoofing attacks) at sensor level is increasing for biometric systems due to the evolving method of artefact presentation. The sophisticated attacks now employ high quality printed artefacts and electronic screens to present the biometric samples which make it difficult to separate the real presentations and artefact presentations. In this work, we propose a new scheme to detect the artefacts in both NIR and visible spectrum biometric sensors for ocular biometric systems using a new set of feature descriptor. The scheme employs adaptive and quantized texture patters obtained from local microfeatures and global spatial features for different color channels in an image. Further, the texture descriptors are used to learn a spectrally regressed discriminant classifier to classify the normal ocular images against the artefact ocular images. The proposed scheme is used to perform extensive experiments on 5 publicly available ocular datasets including 2 datasets acquired in NIR domain and two datasets acquired using smartphones along with a dataset acquired using high quality camera. The experiments conducted on all the datasets have consistently indicated the robust performance against attacks by showing a classification error of 0%.

As biometric authentication methods become more and more common in daily life we have seen an increased interest and developments of more convenient and secure authentication methods for online services. With biometrics enabled smartphones the cost associated with deploying biometric systems is removed and the opportunity for use in new applications is opened. However, with biometrics there are several challenges in terms of security and privacy that must be addressed. In this work we look at two emerging authentication protocols, FIDO Universal Authentication Framework and Biometric Open Protocol Standard, and analyze their security and privacy features from a biometrics perspective.

Iris recognition technologies are deployed in numerous large-scale nation-wide projects in order to provide robust and reliable biometric recognition of individuals. Moreover, the iris has been found to be rather stable over time, i.e. iris biometric reference data provides a strong and permanent link between individuals and their biometric traits. Hence, unprotected storage of (iris) biometric data provokes serious privacy threats, e.g. identity theft, limited re-newability, or cross-matching. Biometric cryptosystems grant a significant improvement in data privacy and increase the likelihood that individuals will effectively consent in the biometric system usage. However, the vast majority of proposed biometric cryptosystems do not guarantee desired properties of irreversibility, unlinkability, and re-newability without significantly degrading the biometric performance. In this work, we propose an unlinkable multi-instance iris biometric cryptosystem based on the improved fuzzy vault scheme. The proposed system locks biometric feature sets extracted from binary iris biometric reference data, i.e. iris-codes, of the left and right irises in a single fuzzy vault. In order to retain the size of the protected template and authentication speed, the proposed fusion step combines the most discriminative parts of two iris-codes at feature level. It is shown that the proposed key-binding process enables the generation of irreversible protected templates which prevents from previously proposed cross-matching attacks. Further, we investigate the optimal choice among potential decoding strategies with respect to biometric performance and time of key retrieval. The fully reproducible system is integrated to two different publicly available iris recognition systems and evaluated on the CASIAv3-Interval and the IITDv1 iris databases. Compared to the corresponding unprotected recognition schemes, genuine match rates of approximately 95 and 97 % at which no false accepts are observed and maintained in a single- and multi-instance scenario, respectively. Moreover, the multi-iris system is shown to significantly improve privacy protection achieving security levels of approximately 70 bits at practical biometric performance.

A light field sensor can provide useful information in terms of multiple depth (or focus) images, holding additional information that is quite useful for biometric applications. In this paper, we examine the applicability of a light field camera for biometric applications by considering two prominently used biometric characteristics: 1) face and 2) iris. To this extent, we employed a Lytro light field camera to construct two new and relatively large scale databases, for both face and iris biometrics. We then explore the additional information available from different depth images, which are rendered by light field camera, in two different manners: 1) by selecting the best focus image from the set of depth images and 2) combining all the depth images using super-resolution schemes to exploit the supplementary information available within the set elements. Extensive evaluations are carried out on our newly constructed database, demonstrating the significance of using additional information rendered by a light field camera to improve the overall performance of the biometric system.

Automatic Border Control (ABC) systems employ biometrics for the identity verification of the travelers. Among the various biometric modalities, the face biometric is predominately used by considering its many advantages including contactless capture, non-intrusive and user-friendly interaction. Since the inflow of passengers at the airport is growing with time, the ABC systems are expected to work with high throughput. One way of addressing this is by performing the on-the-fly face recognition with ABC systems. However, due to various factors, including the change in illumination conditions, it is often challenging to capture a good quality face sample to facilitate a speedy biometric recognition with the ABC systems. One possible way to address this is by employing multiple cameras to capture different views of the face and then combine these views in the face recognition system. In this paper, we propose a framework for combining the information from multiple cameras to improve the face recognition accuracy. To this extent, we investigate five different fusion schemes to present an empirical study on a prototype version of MorphoWay™ ABC system. Extensive experiments are carried out on a database of 61 subjects that are recorded using a prototype version of MorphoWay™ ABC system in three different lighting conditions. The experimental results indicate the improvement in face recognition performance by combing information from multiple cameras in the prototype version of MorphoWay™ ABC system.

The vast majority of speaker recognition cross-entropy evaluations are focused on score domain. By examining the generalized relative distance between genuine and impostor sub-spaces, biometric characteristics become comparable to other authentication approaches. In this paper we demonstrate that the i-vector feature space's biometric information measured by relative entropy is comparable to e.g., knowledge-based mechanisms or face recognition. Examining NIST SRE 2004-2010 corpora, short samples of e.g, 5 seconds duration, comprise already 127 bits in a text-independent scenario. Further, the vast majority of short samples does not fall below 50% of the biometric information of samples having a duration of more than 40 seconds. The generalized i-vector feature space entropy of long samples corresponds to 182.1 bits, and the highest lower entropy bound of a subject was observed at 471.6 bits.

Fingerprint alteration is a type of presentation attack in which the attacker strives to avoid identification, e.g. at border control or in forensic investigations. As a countermeasure, fingerprint alteration detection aims to automatically discover the occurrence of such attacks by classifying fingerprint images as `normal' or `altered'. In this paper, we propose four new features for improving the performance of fingerprint alteration detection modules. We evaluate the usefulness of these features on a benchmark and compare them to four existing features from the literature.

Palmprint is a widely used biometric trait deployed in various access-control applications due to its convenience in use, reliability, and low cost. In this paper, we propose a novel scheme for palmprint recognition using a sparse representation of features obtained from Bank of Binarized Statistical Image Features (B-BSIF). The palmprint image is characterized by a rich set of features including principal lines, ridges, and wrinkles. Thus, the use of an appropriate texture descriptor scheme is expected to capture this information accurately. To this extent, we explore the idea of B-BSIF that comprises of 56 different BSIF filters whose responses on the given palmprint image is processed independently and classified using sparse representation classifier (SRC). Extensive experiments are carried out on three different large-scale publicly available palmprint databases. We then present an extensive analysis by comparing the proposed scheme with seven different contemporary state-of-the-art schemes that reveals the efficacy of the proposed scheme for robust palmprint recognition.

Vulnerability of iris recognition systems remains a challenge due to diverse presentation attacks that fail to assure the reliability when adopting these systems in real-life scenarios. In this paper, we present an in-depth analysis of presentation attacks on iris recognition systems especially focusing on the photo print attacks and the electronic display (or screen) attack. To this extent, we introduce a new relatively large scale visible spectrum iris artefact database comprised of 3300 iris normal and artefact samples that are captured by simulating five different attacks on iris recognition system. We also propose a novel presentation attack detection (PAD) scheme based on multiscale binarized statistical image features and linear support vector machines. Extensive experiments are carried out on four different publicly available iris artefact databases that have revealed the outstanding performance of the proposed PAD scheme when benchmarked with various well-established state-of-the-art schemes.

The gaining popularity of the visible spectrum iris recognition has sparked the interest in adopting it for various access control applications. Along with the popularity of visible spectrum iris recognition comes the threat of identity spoofing, presentation, or direct attack. This paper presents a novel scheme for detecting video presentation attacks in visible spectrum iris recognition system by magnifying the phase information in the eye region of the subject. The proposed scheme employs modified Eulerian video magnification (EVM) to enhance the subtle phase information in eye region and novel decision module to classify it as artefact(spoof attack) or normal presentation. The proposed decision module is based on estimating the change of phase information obtained from EVM, specially tailored to detect presentation attacks on video-based iris recognition systems in visible spectrum. The proposed scheme is extensively evaluated on the newly constructed database consisting of 62 unique iris video acquired using two smartphones-iPhone 5S and Nokia Lumia 1020. We also construct the artefact database with 62 iris acquired by replaying normal presentation iris video on iPad with retina display. Extensive evaluation of proposed presentation attack detection (PAD) scheme on the newly constructed database has shown an outstanding performance of average classification error rate = 0% supporting the robustness of the proposed PAD scheme.

The vulnerability of face recognition systems isa growing concern that has drawn the interest from both academic and research communities. Despite the availability of a broad range of face presentation attack detection (PAD)(or countermeasure or antispoofing) schemes, there exists no superior PAD technique due to evolution of sophisticated presentation attacks (or spoof attacks). In this paper, we present a new perspective for face presentation attack detection by introducing light field camera (LFC). Since the use of a LFC can record the direction of each incoming ray in addition to the intensity, it exhibits an unique characteristic of rendering multiple depth(or focus) images in a single capture. Thus, we present a novel approach that involves exploring the variation of the focus between multiple depth (or focus) images rendered by the LFC that in turn can be used to reveal the presentation attacks. To this extent, we first collect a new face artefact database using LFC that comprises of 80 subjects. Face artefacts are generated by simulating two widely used attacks, such as photo print and electronic screen attack. Extensive experiments carried out on the light field face artefact database have revealed the outstanding performance of the proposed PAD scheme when benchmarked with various well established state-of-the-art schemes.

Using one password for all web services is not secure because the leakage of the password compromises all the web services accounts, while using independent passwords for different web services is inconvenient for the identity claimant to memorize. A password manager is used to address this security-convenience dilemma by storing and retrieving multiple existing passwords using one master password. On the other hand, a password manager liberates human brain by enabling people to generate strong passwords without worry about memorizing them. While a password manager provides a convenient and secure way to managing multiple passwords, it centralizes the passwords storage and shifts the risk of passwords leakage from distributed service providers to a software or token authenticated by a single master password. Concerned about this one master password based security, biometrics could be used as a second factor for authentication by verifying the ownership of the master password. However, biometrics based authentication is more privacy concerned than a non-biometric password manager. In this paper we propose a cloud password manager scheme exploiting privacy enhanced biometrics, which achieves both security and convenience in a privacy-enhanced way. The proposed password manager scheme relies on a cloud service to synchronize all local password manager clients in an encrypted form, which is efficient to deploy the updates and secure against untrusted cloud service providers.

Ear classification refers to the process by which an input ear image is assigned to one of several pre-defined classes based on a set of features extracted from the image. In the context of large-scale ear identification, where the input probe image has to be compared against a large set of gallery images in order to locate a matching identity, classification can be used to restrict the matching process to only those images in the gallery that belong to the same class as the probe. In this work, we utilize an unsupervised clustering scheme to partition ear images into multiple classes (i.e., clusters), with each class being denoted by a prototype or a centroid. A given ear image is assigned class labels (i.e., cluster indices) that correspond to the clusters whose centroids are closest to it. We compare the classification performance of three different texture descriptors, viz. Histograms of Oriented Gradients, uniform Local Binary Patterns and Local Phase Quantization. Extensive experiments using three different ear datasets suggest that the Local Phase Quantization texture descriptor scheme along with PCA for dimensionality reduction results in a 96.89% hit rate (i.e., 3.11% pre-selection error rate) with a penetration rate of 32.08%. Further, we demonstrate that the hit rate improves to 99.01% with a penetration rate of 47.10% when a multi-cluster search strategy is employed.

Presentation (or spoof) attacks on biometric system is a growing concern that received substantial attention from both academics and industry. In this paper, we present a novel way of addressing a Presentation Attack Detection (PAD) (or spoof detection) by exploiting the inherent characteristics of the Light Field Camera (LFC) for visible spectrum iris biometric system. The proposed PAD algorithm will capture the variation in the depth (or focus) between multiple depth images rendered by the LFC that in turn can be used to reveal the presentation attacks. To this extent, we introduce a new presentation attack database comprised of 52 subjects with 104 unique eye samples. The database is collected using LFC by simulating the attacks through visible spectrum iris biometric artefacts like printed photo and electronic display (using both Apple iPad (4thgeneration) and Samsung Galaxy Note 10.1 tablet). Extensive experiments carried out on this database reveal the efficacy of the proposed PAD algorithm with a lowest Average Classification Error Rate = 0.5% when confronted with diverse set of attacks on visible spectrum iris biometric system.

Multimodal biometric systems based on fingerprint and finger vein modality provide promising features useful for robust and reliable identity verification. In this paper, we present a robust imaging device that can capture both fingerprint and finger vein simultaneously. The presented low-cost sensor employs a single camera followed by both near infrared and visible light sources organized along with the physical structures to capture good quality finger vein and fingerprint samples. We further present a novel finger vein recognition algorithm that explores both the maximum curvature method and Spectral Minutiae Representation (SMR). Extensive experiments are carried out on our newly collected database that comprises of 1500 samples of fingerprint and finger vein from 150 unique fingers corresponding to 41 subjects. Our results demonstrate the efficacy of the proposed sensor with a lowest Equal Error Rate of 0.78%.

The performance limitations of the state-of-the-art methods for fingerprint presentation attack detection motivate the application of the Optical Coherence Tomography as the scanning technology capable of capturing all the information necessary for both the fingerprint identification and the presentation attack detection. The previous research has evidenced the need for a reliable technique for assessing quality of the captured scans so that the compliance of the capture subject behavior with acquisition rules could be verified. Good quality of these scans is not only a precondition of good system performance but it also enables reliable processing of the scanned data in the first place. This paper's contribution consists of a novel approach for estimation of the layered structure of an OCT fingerprint scan as a set of analytical surfaces and a subsequent analysis of the quality property of the obtained scan that is related to non-compliant behavior of the capture subjects. The method was able to detect 98% of low-quality scans at a false detection rate of 3%.

The face biometric systems are highly vulnerable for the presentation attack that can be carried out by presenting a photo or video or even a 3D mask. In this paper, we present a novel Presentation Attack Detection (PAD) algorithm that can accurately detect and mitigate the 3D mask attacks on a face recognition system. The proposed scheme extracts both local and global features from the captured face image. The local features employed in this work corresponds to the eye (periocular) and nose region that are expected to provide clue on the presence of the mask. In addition, we also capture the micro-texture variation as a global feature using Binarized Statistical Image Features (BSIF). We then train a linear Support Vector Machine (SVM) independently on these two features whose scores are fused using the weighted sum rule before making the decision about a real face or an artefact. Extensive experiments are carried out on the public 3D mask database 3DMAD that shows the superiority of the proposed scheme with an outstanding performance of HTER = 0.03%.

Biometric data are considered sensitive personal data and any privacy leakage poses severe security risks. Biometric templates should hence be protected, obscuring the biometric signal in a non-reversible manner, while preserving the unprotected system's performance. In the present work, irreversible face templates based on adaptive Bloom filters are proposed. Experiments are carried out on the publicly available Bio Secure DB utilizing the free Bob image processing toolbox, so that research is fully reproducible. The performance and security evaluations proof the irreversibility of the protected templates, while preserving the verification performance. Furthermore, template size is considerably reduced.

The face quality assessment from video must quantitatively measure the applicability of the face images that are typically captured over multiple frames with various degradations. In this work, we address the face quality assessment from the video captured using Automatic Border Control (ABC) system. To this extent, we employed MorphoWayTM ABC system as a data capture device to construct a new database by simulating real-life scenario. We then propose a new scheme for face quality estimation that can be viewed in three steps: (1) Pose estimation by detecting face parts (eyes and nose) to separate frontal from non-frontal faces. (2) We then consider the frontal face and evaluate its corresponding image quality by analyzing its texture components using Grey Level Co-occurrence Matrix (GLCM). (3) Finally, we quantify the quality of the given face image using likelihood values obtained using Gaussian Mixture Model (GMM). Extensive experiments are carried out on our new database that exhibits various quality degradations due to head pose variations, change in illumination, expression, motion blur, etc. The experimental results have indicated that the proposed face quality assessment algorithm can effectively classify the input image into relevant quality bins that in turn can be employed for the improved face verification.

This paper proposes a new scheme for robust palmprint verification using sparse representation of Binarized Statistical Image Features (BSIF). Since palmprint comprises of rich set of features including principal lines, ridges and wrinkles, the use of appropriate texture descriptor is expected to accurately capture these information. To this extent, we explore the BSIF texture descriptor which codes each pixel of the given palmprint image in terms of binary strings based on the filter response. The BSIF learns the filter basis from the natural images by exploring statistical independence. We then use the Sparse Representation Classifier (SRC) on these BSIF features to perform the subject verification. Extensive experiments are carried out on three different large scale publically available palmprint databases. We then present an extensive analysis by comparing the proposed scheme with five different contemporary state-of-the-art schemes that reveals the outstanding performance.

The advanced technologies and sensors in smartphones has led to showcase their potential as a biometric sensor. In this work, we present the feasibility study and challenges in the path forward for using smartphone as a biometric sensor for iris recognition in visible spectrum. Especially, with a limited shelf-life of smartphones, it is anticipated to have enrolment and verification using different camera. In this work, we propose an improvement to segmentation scheme for contactless iris acquisition by approximating the radius range. The proposed method has resulted in a segmentation accuracy of 81%. We also propose various protocols for real-life verification scenarios using smartphones for visible spectrum iris recognition. Finally, results from an extensive set of experiments are presented to validate the anticipated challenges in using smartphone based iris recognition. Being the first of its kind, this work provides the benchmarking results for the smartphone iris database. The best EER is obtained for iPhone in indoor scenario with an impressive EER of 0.48%.

This paper proposes an approach to qualifying fingerprint samples captured by smartphone cameras under real-life scenarios, foreseeing the future application using such general purposed cameras as fingerprint sensors. In this approach, a sample image is first divided into non-overlapping blocks. Then a 7-dimensional feature vector will be formed from the proposed 7 quality features. We use a support vector machine to produce a binary indication for each image block on its quality. Finally a quality score is generated to indicate the whole fingerprint sample's quality by counting the number of qualified blocks in a sample. Experiments demonstrate the proposed approach's capability of qualifying such quality-challenging fingerprint samples - the Spearman's rank correlation coefficient ρ between the proposed quality metric and samples' normalized comparison scores reaches as high as 0.53 in our experiment.

In this paper, we present an empirical study on exploring the Light Field Camera (LFC) for identifying multiple faces present at different distance. Since LFC can render multiple focus images in single exposure, one can combine these multiple images to obtain single all-in-focus image. Thus the constructed all-in-focus image will have all regions in focus and hence allows one to capture more information about the subject present even at a far distance. At the same time one can also construct the super resolution image to further improve the face recognition at a distance. Thus, in this work, we explore both all-in-focus and super resolution schemes to evaluate the multiple face recognition at a distance using LFC. We carry out extensive experiments on light field face dataset and present both qualitative and quantitative results.

Similar to biometric fingerprint recognition, characteristic minutiae points - here end - and branch points - can be extracted from skeletonized veins to distinguish individuals. An approach to extract those vein minutiae and to transform them into a fixed-length, translation and scale in variant representation where rotations can be easily compensated is presented in this paper. The proposed solution based on spectral minutiae is evaluated against other comparison strategies on three different datasets of wrist and palm vein samples. It shows a competitive biometric performance while producing features that are compatible with state-of-the-art template protection systems.

Fuzzy commitment is an efficient template protection algorithm that can improve security and safeguard privacy of biometrics. Existing theoretical security analysis has proved that although privacy leakage is unavoidable, perfect security from information-theoretical points of view is possible when bits extracted from biometric features are uniformly and independently distributed. Unfortunately, this strict condition is difficult to fulfill in practice. In many applications, dependency of binary features is ignored and security is thus suspected to be highly overestimated. This paper gives a comprehensive analysis on security and privacy of fuzzy commitment regarding empirical evaluation. The criteria representing requirements in practical applications are investigated and measured quantitatively in an existing protection system for 3D face recognition. The evaluation results show that a very significant reduction of security and enlargement of privacy leakage occur due to the dependency of biometric features. This work shows that in practice, one has to explicitly measure the security and privacy instead of trusting results under non-realistic assumptions.

A local-area based fingerprint minutiae vicinity can be represented in a self-aligned way and achieves better robustness for recognition than a global minutiae template whose global geometric references (e.g., core or delta) are usually unstable to locate. However, local comparison based on vicinities ignores the global topology and thus still has potential to improve in performance if some stable global information can be fused in vicinities. We construct in this paper an augmented minutia vicinity, which incorporates more contextual minutiae information. Application of the proposed augmented vicinity to minutiae template protection is tested and demonstrates desirable biometric performance (e.g., FRR = 0.04-0.06 and FAR = 0.001 on the FVC2002DB2_A database) with roughly 70-bit complexity against reversing a binary protected augmented vicinity.

A renewable fingerprint minutiae template generation scheme is proposed to utilize random projection for template diversification in a security enhanced way. The scheme first achieves absolute pre-alignment over local minutiae quadruplets in the original template and results in a fix-length feature vector; and then encrypts the feature vector by projecting it to multiple random matrices and quantizing the projected result; and finally post-process the resultant binary vector in a size and security tunable way to obtain the final protected minutia vicinity. Experiments on the fingerprint database FVC2002DB2_A demonstrate the desirable biometric performance of the proposed scheme.

Often in the development of a biometric product an evaluator of the system is the same entity who developed the algorithm. Moreover, usually the test data employed in such evaluation is also collected by the same developer/evaluator. In most cases such database will not be made public and consequently test results cannot be verified by independent institutions. This paper presents an independent report on fingerprint performance evaluation that has been conducted in the context of the TURBINE project. In this study the algorithm developer and system evaluator are represented by separate and independent entities. In addition, the algorithm developer does not have access to the primary test database. All these provide pre-conditions to unbiased and trustworthy performance reports. Furthermore, this paper introduces biometric performance testing on a level of biometric references, which is complementary to image- or minutiae-based references. Biometric references in the TURBINE project are pseudonymous identifiers that have been generated by the template protection algorithms. The results of the performance evaluation in this paper are generated by applying the algorithm developers (binary) algorithms at the minutiae (traditional) and pseudonymous identifier levels. The test data set consists of almost 72000 fingerprint images from 100 subjects acquired by several fingerprint scanners to which the algorithm developers did not have access.

A novel multimodal biometric recognition algorithm based on complex kernel fisher discriminant analysis (complex KFDA) is proposed. Complex KFDA exploits two phases to generalize KFDA and perform classification for the fusion feature set: complex KPCA plus complex LDA. As two distinct biometric modals, the features of iris and face are fused in parallel to test our algorithm. Experimental results show that the proposed algorithm achieves much better performance than other conventional multimodal biometric algorithms.

Biometric technologies are becoming much more important in various applications. Among them, iris recognition is considered as one of the most reliable and accurate technologies. In the preparation of iris recognition, the iris location will influence the performance of the entire system. This paper proposes a novel algorithm to locate iris and eyelids. Morphological operation is applied to remove eyelashes during iris boundary location. An optimal step length is calculated to reduce the searching time. Experimental results demonstrate that the proposed iris location algorithm is able to achieve a good performance with accuracy higher than 97.6%.

We present a reversible watermarking scheme which achieves perfect restoration of both the embedded watermark and the original image during extraction. The proposed scheme embeds data by modifying those integer DCT coefficients with peak amplitudes in each coefficient histogram. The integer DCT performed over the original image is a lossless 8/spl times/8 block transform with high energy concentrating ability, which guarantees reversibility and high capacity/distortion ratio for the proposed watermarking scheme. In addition, this scheme provides a wide quality (PSNR) range from around 40 dB to 60 dB for the watermarked image, and an inherent fine adjustment capability for the quality (PSNR). Some experimental results are presented to demonstrate the high performance of our scheme in terms of capacity and the quality of the watermarked image.

This paper presents a security system for enforcing security policies throughout distributed environments. The aspects of the system dealing with the protection of digital data using object labeling and mandatory encryption at the OS level are covered briefly; the main focus is on protection provided in the analog domain. This is accomplished by embedding multiple watermarks identifying the copyright owner, the identity of the object, and of users accessing the object into any markable object accessed by users.

We describe a Digital Watermarking system dedicated for embedding watermarks into 3D polygonal models. The system consists of three watermarking algorithms, one named Vertex Flood Algorithm (VFA) suitable for embedding fragile public readable watermarks with high capacity and offering a way of model authentication, one realizing affine invariant watermarks, named Affine Invariant Embedding (AIE) and a third one, named Normal Bin Encoding (NBE) algorithm, realizing watermarks with robustness against more complex operations, most noticeably polygon reduction. The watermarks generated by these algorithms are stackable. We shortly discuss the implementation of the system, which is realized as a 3D Studio MAX plugin.

The paper considers the computer-based support for the localization of pathological tissue within tomographic data. The subject of the approach is the inter-patient analysis of brain tissue types such as tumor, CSF, white matter, grey matter, bone, fat tissue and background. The class tumor hereby represents the superset of pathological tissue. The analysis pipeline of the presented approach contains feature extraction, classification, two-step texture analysis and morphological postprocessing. Furthermore the paper reports results that have been reached on the different steps of the pipeline.

Abstract The subject of this paper is the semantically based postprocessing of color–coded images such as classification results. We outline why the classical definition of mathematical morphology suffers if it is used for processing of coded image data. Therefore we provide an extension for morphological operations such as dilation, erosion, opening, and closing. With a new understanding of morphology we introduce bridging and tunneling as further combinations of dilation and erosion. The extensions are applied to medical image data, where the semantic rules stem from basic anatomical knowledge.

Abstract The following paper describes a new approach for the automatic segmentation and tissue classification of anatomical objects such as brain tumors from magnetic resonance imaging (MRI) data sets using artificial neural networks. These segmentations serve as an input for 3D–reconstruction algorithms. Since MR images require a careful interpretation of the underlying physics and parameters, we first give the reader a tutorial style introduction to the physical basics of MR technology. Secondly, we describe our approach that is based on a two–pass method including non–supervised cluster analysis, dimensionality reduction and visualization of the texture features by means of nonlinear topographic mappings. An additional classification of the MR data set can be obtained using a post–processing technique to approximate the Bayes decision boundaries. Interactions between the user and the network allow an optimization of the results. For fast 3D–reconstructions, we use a modified marching cubes algorithm but our scheme can easily serve as a preprocessor for any kind of volume renderer. The applications we present in our paper aim at the automatic extraction and fast reconstruction of brain tumors for surgery and therapy planning. We use the neural networks on pathological data sets and show how the method generalizes to physically comparable data sets.