In today’s data-driven analytics landscape, deep learning has become a powerful tool, with latent representations, known as embeddings, playing a central role in several applications. In the face analytics domain, such embeddings are commonly used for biometric recognition (e.g., face identification). However, these embeddings, or templates, can inadvertently expose sensitive attributes such as age, gender, and ethnicity. Leaking such information can compromise personal privacy and affect civil liberty and human rights. To address these concerns, we introduce a multi-layer protection framework for embeddings. It consists of a sequence of operations: (a) encrypting embeddings using Fully Homomorphic Encryption (FHE), and (b) hashing them using irreversible feature manifold hashing. Unlike conventional encryption methods, FHE enables computations directly on encrypted data, allowing downstream analytics while maintaining strong privacy guarantees. To reduce the overhead of encrypted processing, we employ embedding compression. Our proposed method shields latent representations of sensitive data from leaking private attributes (such as age and gender) while retaining essential functional capabilities (such as face identification). Extensive experiments on two datasets using two face encoders demonstrate that our approach outperforms several state-of-the-art privacy protection methods.

A facial morph is an image strategically created by combining two face images pertaining to two distinct identities. The goal is to create a face image that can be matched to two different identities by a face matcher. Face demorphing inverts this process and attempts to recover the original images constituting a facial morph. Existing demorphing techniques have two major limitations: (a) they assume that some identities are common in the train and test sets; and (b) they are prone to the morph replication problem, where the outputs are merely replicates of the input morph. In this paper, we overcome these issues by proposing dc-GAN (dual-conditioned GAN), a novel demorphing method conditioned on the morph image as well as the embedding extracted from the image. Our method overcomes the morph replication problem and produces high-fidelity reconstructions of the constituent images. Moreover, the proposed method is highly generalizable and applicable to both reference-based and reference-free demorphing methods. Experiments were conducted using the AMSL, FRLL-Morphs, and MorDiff datasets to demonstrate the efficacy of the method.

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

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

A morph is created by combining two (or more) face images from two (or more) identities to create a composite image that is highly similar to all constituent identities, allowing the forged morph to be biometrically associated with more than one individual. Morph Attack Detection (MAD) can be used to detect a morph, but does not reveal the constituent images. Demorphing - the process of deducing the constituent images - is thus vital to provide additional evidence about a morph. Existing demorphing methods suffer from the morph replication problem, where the outputs tend to look very similar to the morph itself, or assume that train and test morphs are generated using the same morph technique. The proposed method overcomes these issues. The method decomposes a morph in latent space allowing it to demorph images created from unseen morph techniques and face styles. We train our method on morphs created from synthetic faces and test on morphs created from real faces using different morph techniques. Our method outperforms existing methods by a considerable margin and produces high fidelity demorphed face images.

A face morph is created by combining two (or more) face images corresponding to two (or more) identities to produce a composite that successfully matches the constituent identities. Reference-free (RF) demorphing reverses this process using only the morph image, without the need for additional reference images. Previous RF demorphing methods were overly constrained, as they rely on assumptions about the distributions of training and testing morphs such as the morphing technique used, face style, and images used to create the morph. In this paper, we introduce a novel diffusion-based approach, referred to as diffDeMorph, that effectively disentangles component images from a composite morph image with high visual fidelity. Our method is the first to generalize across morph techniques and face styles, beating the current state of the art by ≥ 59.46% under a common training protocol across all datasets tested. We train our method on morphs created using synthetically generated face images and test on real morphs, thereby enhancing the practicality of the technique. Experiments on six datasets and two face matchers establish the utility and efficacy of our method.

Iris-based biometric systems are vulnerable to presentation attacks (PAs), where adversaries present physical artifacts (e.g., printed iris images, textured contact lenses) to defeat the system. This has led to the development of various presentation attack detection (PAD) algorithms, which typically perform well in intra-domain settings. However, they often struggle to generalize effectively in cross-domain scenarios, where training and testing employ different sensors, PA instruments, and datasets. In this work, we use adversarial training samples of both bonafide irides and PAs to improve the cross-domain performance of a PAD classifier. The novelty of our approach lies in leveraging transformation parameters from classical data augmentation schemes (e.g., translation, rotation) to generate adversarial samples. We achieve this through a convolutional au-toencoder, ADV-GEN, that inputs original training samples along with a set of geometric and photometric transformations. The transformation parameters act as regularization variables, guiding ADV-GEN to generate adversarial samples in a constrained search space. Experiments conducted on the LivDet-Iris 2017 database, comprising four datasets, and the LivDet-Iris 2020 dataset, demonstrate the efficacy of our proposed method. The code is available at https://github.com/iPRoBe-lab/ADV-GEN-IrisPAD.

It was our pleasure and privilege to welcome you to Istanbul for the 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024). We hope your experience at FG was rewarding both professionally and personally!

Modern face recognition systems utilize deep neural networks to extract salient features from a face. These features denote embeddings in latent space and are often stored as templates in a face recognition system. These embeddings are susceptible to data leakage and, in some cases, can even be used to reconstruct the original face image. To prevent compromising identities, template protection schemes are commonly employed. However, these schemes may still not prevent the leakage of soft biometric information such as age, gender and race. To alleviate this issue, we propose a novel technique that combines Fully Homomorphic Encryption (FHE) with an existing template protection scheme known as PolyProtect. We show that the embeddings can be compressed and encrypted using FHE and transformed into a secure PolyProtect template using polynomial transformation, for additional protection. We demonstrate the efficacy of the proposed approach through extensive experiments on multiple datasets. Our proposed approach ensures irreversibility and unlinkability, effectively preventing the leakage of soft biometric attributes from face embeddings without compromising recognition accuracy.

This study utilizes the advanced capabilities of the GPT-4 multimodal Large Language Model (LLM) to explore its potential in iris recognition — a field less common and more specialized than face recognition. By focusing on this niche yet crucial area, we investigate how well AI tools like ChatGPT can understand and analyze iris images. Through a series of meticulously designed experiments employing a zero-shot learning approach, the capabilities of ChatGPT-4 was assessed across various challenging conditions including diverse datasets, presentation attacks, occlusions such as glasses, and other real-world variations. The findings convey ChatGPT-4’s remarkable adaptability and precision, revealing its proficiency in identifying distinctive iris features, while also detecting subtle effects like makeup on iris recognition. A comparative analysis with Gemini Advanced – Google’s AI model – highlighted ChatGPT-4’s better performance and user experience in complex iris analysis tasks. This research not only validates the use of LLMs for specialized biometric applications but also emphasizes the importance of nuanced query framing and interaction design in extracting significant insights from biometric data. Our findings suggest a promising path for future research and the development of more adaptable, efficient, robust and interactive biometric security solutions.

A face morph is created by combining the face images usually pertaining to two distinct identities. The goal is to generate an image that can be matched with two identities thereby undermining the security of a face recognition system. To deal with this problem, several morph attack detection techniques have been developed. But these methods do not extract any information about the underlying bonafides used to create them. Demorphing addresses this limitation. However, current demorphing techniques are mostly reference-based, i.e, they need an image of one of the identities to recover the other. In this work, we treat demorphing as an ill-posed decomposition problem. We propose a novel method that is reference-free and recovers the bonafides with high accuracy. Our method decomposes the morph into several identity-preserving feature components. A merger network then weighs and combines these components to recover the bonafides. Our method is observed to reconstruct high-quality bonafides in terms of definition and fidelity. Experiments on the CASIA-WebFace, SMDD and AMSL datasets demonstrate the effectiveness of our method.

Whole-body biometric recognition is an important area of research due to its vast applications in law enforcement, border security, and surveillance. This paper presents the end-to-end design, development and evaluation of FarSight, an innovative software system designed for whole-body (fusion of face, gait and body shape) biometric recognition. FarSight accepts videos from elevated platforms and drones as input and outputs a candidate list of identities from a gallery. The system is designed to address several challenges, including (i) low-quality imagery, (ii) large yaw and pitch angles, (iii) robust feature extraction to accommodate large intra-person variabilities and large inter-person similarities, and (iv) the large domain gap between training and test sets. FarSight combines the physics of imaging and deep learning models to enhance image restoration and biometric feature encoding. We test FarSight’s effectiveness using the newly acquired IARPA Biometric Recognition and Identification at Altitude and Range (BRIAR) dataset. Notably, FarSight demonstrated a substantial performance increase on the BRIAR dataset, with gains of +11.82% Rank-20 identification and +11.30% TAR@1% FAR.

There has been tremendous progress in generating realistic faces with high fidelity over the past few years. Despite this progress, a crucial question remains unanswered: “Given a generative face model, how many unique identities can it generate?” In other words, what is the biometric capacity of the generative face model? A scientific basis for answering this question will benefit evaluating and comparing different generative face models and establish an upper bound on their scalability. This paper proposes a statistical approach to estimate the biometric capacity of generated face images in a hyperspherical feature space. We employ our approach on multiple generative models, including unconditional generators like StyleGAN, Latent Diffusion Model, and “Generated Photos,” as well as DCFace, a class-conditional generator. We also estimate capacity w.r.t. demographic attributes such as gender and age. Our capacity estimates indicate that (a) under ArcFace representation at a false acceptance rate (FAR) of 0.1%, StyleGAN3 and DCFace have a capacity upper bound of $1.43 \times 10^{6}$ and $1.190 \times 10^{4}$, respectively; (b) the capacity reduces drastically as we lower the desired FAR with an estimate of $1.796 \times 10^{4}$ and 562 at FAR of 1% and 10%, respectively, for StyleGAN3; (c) there is no discernible disparity in the capacity w.r.t gender; and (d) for some generative models, there is an appreciable disparity in the capacity w.r.t age. Code is available at https://github.com/humananalysis/capacity-generative-face-models.

Person re-identification (Re-ID) on aerial-ground platforms has emerged as an intriguing topic within computer vision, presenting a plethora of unique challenges. Highflying altitudes of aerial cameras make persons appear differently in terms of viewpoints, poses, and resolution compared to the images of the same person viewed from ground cameras. Despite its potential, few algorithms have been developed for person re-identification on aerial-ground data, mainly due to the absence of comprehensive datasets. In response, we have collected a large-scale dataset and organized the Aerial-Ground person Re-IDentification Challenge (AG-ReID2023) to foster advancements in the field. The dataset comprises 100,502 images with 1,615 unique identities, including 51,530 training images featuring 807 identities. The test set is divided into two subsets: Aerial to Ground (808 ids, 4,348 query images, 19,259 gallery images) and Ground to Aerial (808 ids, 4,151 query images, 21,214 gallery images). In addition, we manually annotate individuals with their matching IDs across cameras and provide 15 soft attribute labels. The AG-ReID2023 Challenge in conjunction with the 7thIEEE International Joint Conference on Biometrics (IJCB) has garnered interest from numerous institutes, resulting in the submission of five distinct algorithms. We provide an in-depth examination of the evaluation outcomes and present our findings from the contest. For additional details, kindly refer to the official website1.1https://agreid23.github.io.

The accuracy of automated speaker recognition is negatively impacted by change in emotions in a person’s speech. In this paper, we hypothesize that speaker identity is composed of various vocal style factors that may be learned from unlabeled data and re-combined using a neural network to generate a holistic speaker identity representation for affective scenarios. In this regard, we propose the E-Vector architecture, composed of a 1-D CNN for learning speaker identity features and a vocal style factorization technique for determining vocal styles. Experiments conducted on the MSP-Podcast dataset demonstrate that the proposed architecture improves state-of-the-art speaker recognition accuracy in the affective domain over baseline ECAPA-TDNN speaker recognition models. For instance, the true match rate at a false match rate of 1% improves from 27.6% to 46.2%.

Generative Adversarial Networks (GANs) have shown success in approximating complex distributions for synthetic image generation. However, current GAN-based methods for generating biometric images, such as iris, have certain limitations: (a) the synthetic images often closely resemble images in the training dataset; (b) the generated images lack diversity in terms of the number of unique identities represented in them; and (c) it is difficult to generate multiple images pertaining to the same identity. To overcome these issues, we propose iWarpGAN that disentangles identity and style in the context of the iris modality by using two transformation pathways: Identity Transformation Pathway to generate unique identities from the training set, and Style Transformation Pathway to extract the style code from a reference image and output an iris image using this style. By concatenating the transformed identity code and reference style code, iWarpGAN generates iris images with both inter- and intra-class variations. The efficacy of the proposed method in generating such iris Deep-Fakes is evaluated both qualitatively and quantitatively using ISO/IEC 29794-6 Standard Quality Metrics and the Ver-iEye iris matcher. Further, the utility of the synthetically generated images is demonstrated by improving the performance of deep learning based iris matchers that augment synthetic data with real data during the training process.

As AI-enhanced technologies become common in a variety of domains, there is an increasing need to define and examine the trust that users have in such technologies. Given the progress in the development of AI, a correspondingly sophisticated understanding of trust in the technology is required. This paper addresses this need by explaining the role of trust in the intention to use AI technologies. Study 1 examined the role of trust in the use of AI voice assistants based on survey responses from college students. A path analysis confirmed that trust had a significant effect the on intention to use AI, which operated through perceived usefulness and participants’ attitude toward voice assistants. In Study 2, using data from a representative sample of the U.S. population, different dimensions of trust were examined using exploratory factor analysis, which yielded two dimensions: human-like trust and functionality trust. The results of the path analyses from Study 1 were replicated in Study 2, confirming the indirect effect of trust and the effects of perceived usefulness, ease of use, and attitude on intention to use. Further, both dimensions of trust shared a similar pattern of effects within the model, with functionality-related trust exhibiting a greater total impact on usage intention than human-like trust. Overall, the role of trust in the acceptance of AI technologies was significant across both studies. This research contributes to the advancement and application of the TAM in AI-related applications and offers a multidimensional measure of trust that can be utilized in the future study of trustworthy AI.

In this work, we design a fully complex-valued neural network for the task of iris recognition. Unlike the problem of general object recognition, where real-valued neural networks can be used to extract pertinent features, iris recognition depends on the extraction of both phase and magnitude information from the input iris texture in order to better represent its biometric content. This necessitates the extraction and processing of phase information that cannot be effectively handled by a real-valued neural network. In this regard, we design a fully complex-valued neural network that can better capture the multi-scale, multi-resolution, and multi-orientation phase and amplitude features of the iris texture. We show a strong correspondence of the proposed complex-valued iris recognition network with Gabor wavelets that are used to generate the classical IrisCode; however, the proposed method enables a new capability of automatic complex-valued feature learning that is tailored for iris recognition. We conduct experiments on three benchmark datasets - ND-CrossSensor-2013, CASIA-Iris-Thousand and UBIRIS.v2 - and show the benefit of the proposed network for the task of iris recognition. We exploit visualization schemes to convey how the complex-valued network, when compared to standard real-valued networks, extracts fundamentally different features from the iris texture.

In this work, we study the effect of speaking style and audio condition variability between the spoken and singing voice on speaker recognition performance. Furthermore, we also explore the utility of domain adaptation for bridging the gap between multiple speaking styles (singing versus spoken) and improving overall speaker recognition performance. In that regard, we first extend a publicly available singing voice dataset, JukeBox, with corresponding spoken voice data and refer to it as JukeBox-V2. Next, we use domain adaptation for developing a speaker recognition method robust to varying speaking styles and audio conditions. Finally, we analyze the speech embeddings of domain-adapted models to explain their generalizability across varying speaking styles and audio conditions.

A face morph is created by strategically combining two or more face images corresponding to multiple identities. The intention is for the morphed image to match with multiple identities. Current morph attack detection strategies can detect morphs but cannot recover the images or identities used in creating them. The task of deducing the individual face images from a morphed face image is known as demorphing. Existing work in de-morphing assume the availability of a reference image pertaining to one identity in order to recover the image of the accomplice - i.e., the other identity. In this work, we propose a novel de-morphing method that can recover images of both identities simultaneously from a single morphed face image without needing a reference image or prior information about the morphing process. We propose a generative adversarial network that achieves single image-based de-morphing with a surprisingly high degree of visual realism and biometric similarity with the original face images. We demonstrate the performance of our method on landmark-based morphs and generative model-based morphs with promising results.

This paper proposes a non-interactive end-to-end solution for secure fusion and matching of biometric templates using fully homomorphic encryption (FHE). Given a pair of encrypted feature vectors, we perform the following ciphertext operations, i) feature concatenation, ii) fusion and dimensionality reduction through a learned linear projection, iii) scale normalization to unit ℓ2-norm, and iv) match score computation. Our method, dubbed HEFT (Homomorphi-cally Encrypted Fusion of biometric Templates), is custom-designed to overcome the unique constraint imposed by FHE, namely the lack of support for non-arithmetic operations. From an inference perspective, we systematically explore different data packing schemes for computationally efficient linear projection and introduce a polynomial approximation for scale normalization. From a training perspective, we introduce an FHE-aware algorithm for learning the linear projection matrix to mitigate errors induced by approximate normalization. Experimental evaluation for template fusion and matching of face and voice biometrics shows that HEFT (i) improves biometric verification performance by 11.07% and 9.58% AUROC compared to the respective unibiometric representations while compressing the feature vectors by a factor of 16 (512D to 32D), and (ii) fuses a pair of encrypted feature vectors and computes its match score against a gallery of size 1024 in 884 ms. Code and data are available at https://github.com/humananalysis/encrypted-biometric-fusion

The use of multiple modalities (e.g., face and fingerprint) or multiple algorithms (e.g., three face comparators) has shown to improve the recognition accuracy of an operational biometric system. Over time a biometric system may evolve to add new modalities, retire old modalities, or be merged with other biometric systems. This can lead to scenarios where there are missing scores corresponding to the input probe set. Previous work on this topic has focused on either the verification or identification tasks, but not both. Further, the proportion of missing data considered has been less than 50%. In this work, we study the impact of missing score data for both the verification and identification tasks. We show that the application of various score imputation methods along with simple sum fusion can improve recognition accuracy, even when the proportion of missing scores increases to 90%. Experiments show that fusion after score imputation outperforms fusion with no imputation. Specifically, iterative imputation with K nearest neighbors consistently surpasses other imputation methods in both the verification and identification tasks, regardless of the amount of scores missing, and provides imputed values that are consistent with the ground truth complete dataset.

Medical diagnosis involves the expert opinion of trained health care professionals based on causal inference from medical data. While medical data are typically collected using specialized medical-grade sensors, similar data characteristics useful for medical diagnosis are sometimes present in biometric data (e.g., face images, ocular images, and speech signals). In this paper, we explore the biometrics and medical literature to study the following questions. 1) What kind of health cues are embedded in the commonly utilized forms of audio-visual biometric data? 2) How can these health cues be gleaned from the biometric data, and what kind of diseases can it help diagnose? 3) What are some of the implications of using biometric data for medical diagnosis?

One of the main challenges in performing thermal-to-visible face image translation is preserving the identity across different spectral bands. Existing work does not effectively disentangle the identity from other confounding factors. In this paper, we propose a Latent-Guided Generative Adversarial Network (LG-GAN) to explicitly decompose an input image into identity code that is spectral-invariant and style code that is spectral-dependent. By using such a disentanglement, we are able to analyze the identity preservation by interpreting and visualizing the identity code. We present extensive face recognition experiments on two challenging Visible-Thermal face datasets. We show that the learned identity code is effective in preserving the identity, thus offering useful insights on interpreting and explaining thermal-to-visible face image translation.

Automatic speaker recognition algorithms typically characterize speech audio using short-term spectral features that encode the physiological and anatomical aspects of speech production. Such algorithms do not fully capitalize on speaker-dependent characteristics present in behavioral speech features. In this work, we propose a prosody encoding network called DeepTalk for extracting vocal style features directly from raw audio data. The DeepTalk method outperforms several state-of-the-art speaker recognition systems across multiple challenging datasets. The speaker recognition performance is further improved by combining DeepTalk with a state-of-the-art physiological speech feature-based speaker recognition system. We also integrate DeepTalk into a current state-of-the-art speech synthesizer to generate synthetic speech. A detailed analysis of the synthetic speech shows that the DeepTalk captures F0 contours essential for vocal style modeling. Furthermore, DeepTalk-based synthetic speech is shown to be almost indistinguishable from real speech in the context of speaker recognition.

We present the task of differential face morph attack detection using a conditional generative network (cGAN). To determine whether a face image in an identification document, such as a passport, is morphed or not, we propose an algorithm that learns to implicitly disentangle identities from the morphed image conditioned on the trusted reference image using the cGAN. Furthermore, the proposed method can also recover some underlying information about the second subject used in generating the morph. We performed experiments on AMSL face morph, MorGAN, and EMorGAN datasets to demonstrate the effectiveness of the proposed method. We also conducted cross-dataset and cross-attack detection experiments. We obtained promising results of 3% BPCER @ 10% APCER on intra-dataset evaluation, which is comparable to existing methods; and 4.6% BPCER @ 10% APCER on cross-dataset evaluation, which outperforms state-of-the-art methods by at least 13.9%.

We investigate the problem of morph attacks in the context of iris biometrics. A morph attack entails the generation of an image that embodies two different identities. This is accomplished by combining, i.e., morphing, two biometric samples pertaining to two different identities. While such an attack is being increasingly studied in the context of face recognition, it has not been widely analyzed in iris recognition. In this work, we perform iris morphing at the image-level and generate morphed iris images using two available datasets (IITD and WVU multi-modal). We demonstrate the vulnerability of three different iris recognition methods to morph attacks with a success rate of over 90% at a false match rate of 0.01%. We also analyze the textural similarity required between the component images to create a successful morphed image. Finally, we provide preliminary results on the detection of morphed iris images.

In this work, we propose a novel Cyclic Image Translation Generative Adversarial Network (CIT-GAN) for multi-domain style transfer. To facilitate this, we introduce a Styling Network that has the capability to learn style characteristics of each domain represented in the training dataset. The Styling Network helps the generator to drive the translation of images from a source domain to a reference domain and generate synthetic images with style characteristics of the reference domain. The learned style characteristics for each domain depend on both the style loss and domain classification loss. This induces variability in style characteristics within each domain. The proposed CIT-GAN is used in the context of iris presentation attack detection (PAD) to generate synthetic presentation attack (PA) samples for classes that are under-represented in the training set. Evaluation using current state-of-the-art iris PAD methods demonstrates the efficacy of using such synthetically generated PA samples for training PAD methods. Further, the quality of the synthetically generated samples is evaluated using Frechet Inception Distance (FID) score. Results show that the quality of synthetic images generated by the proposed method is superior to that of other competing methods, including StarGan.

Biometric recognition technology has made significant advances over the last decade and is now used across a number of services and applications. However, this widespread deployment has also resulted in privacy concerns and evolving societal expectations about the appropriate use of the technology. For example, the ability to automatically extract age, gender, race, and health cues from biometric data has heightened concerns about privacy leakage. Face recognition technology, in particular, has been in the spotlight, and is now seen by many as posing a considerable risk to personal privacy. In response to these and similar concerns, researchers have intensified efforts towards developing techniques and computational models capable of ensuring privacy to individuals, while still facilitating the utility of face recognition technology in several application scenarios. These efforts have resulted in a multitude of privacy-enhancing techniques that aim at addressing privacy risks originating from biometric systems and providing technological solutions for legislative requirements set forth in privacy laws and regulations, such as GDPR. The goal of this overview paper is to provide a comprehensive introduction into privacy-related research in the area of biometrics and review existing work on Biometric Privacy-Enhancing Techniques (B-PETs) applied to face biometrics. To make this work useful for as wide of an audience as possible, several key topics are covered as well, including evaluation strategies used with B-PETs, existing datasets, relevant standards, and regulations and critical open issues that will have to be addressed in the future.

Welcome to the 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020). FG is the premier international conference on vision-based automatic face and body behavior analysis and applications. Since its first meeting in Zurich in 1994, the conference has been held fourteen times throughout the world. This is the 15th conference.

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

In this work, we investigate the possibility of generating a grayscale image of the finger vein from its binary template. This exercise would allow us to determine the invertibility of finger vein templates, and this has implications in biometric security and privacy. While such an analysis has been undertaken in the context of face, fingerprint and iris templates, this is the first work involving the finger vein biometric trait. The transformation from binary features to a grayscale image is accomplished using a Pix2Pix Convolutional Neural Network (CNN). The reversibility of 6 different types of binary features is evaluated using this CNN. Further, a number of experiments are conducted using 7 distinct finger vein datasets. Results indicate that (a) it is possible to reconstruct finger vein images from their binary templates; (b) the reconstructed images can be used for biometric recognition purposes; (c) the CNN trained on one dataset can be successfully used for reconstructing images in a different dataset (cross-dataset reconstruction); and (d) the images reconstructed from one set of features can be successfully used to extract a different set of features for biometric recognition (cross-feature-set generalization).

An iris recognition system is vulnerable to presentation attacks, or PAs, where an adversary presents artifacts such as printed eyes, plastic eyes, or cosmetic contact lenses to circumvent the system. In this work, we propose an effective and robust iris PA detector called D-NetPAD based on the DenseNet convolutional neural network architecture. It demonstrates generalizability across PA artifacts, sensors and datasets. Experiments conducted on a proprietary dataset and a publicly available dataset (LivDet-2017) substantiate the effectiveness of the proposed method for iris PA detection. The proposed method results in a true detection rate of 98.58% at a false detection rate of 0.2% on the proprietary dataset and outperforms state-of-the-art methods on the LivDet-2017 dataset. We visualize intermediate feature distributions and fixation heatmaps using t-SNE plots and Grad-CAM, respectively, in order to explain the performance of D-NetPAD. Further, we conduct a frequency analysis to explain the nature of features being extracted by the network. The source code and trained model are available at https://github.com/iPRoBe-lab/D-NetPAD.

In this work, we propose a method to simultaneously perform (i) biometric recognition (i.e., identify the individual), and (ii) device recognition, (i.e., identify the device) from a single biometric image, say, a face image, using a one-shot schema. Such a joint recognition scheme can be useful in devices such as smartphones for enhancing security as well as privacy. We propose to automatically learn a joint representation that encapsulates both biometric-specific and sensor-specific features. We evaluate the proposed approach using iris, face and periocular images acquired using near-infrared iris sensors and smartphone cameras. Experiments conducted using 14,451 images from 13 sensors resulted in a rank-1 identification accuracy of upto 99.81% and a verification accuracy of upto 100% at a false match rate of 1%.

Existing key-binding biometric cryptosystems, such as the Fuzzy Vault Scheme (FVS) and Fuzzy Commitment Scheme (FCS), employ Error Correcting Codes (ECC) to handle intra-user variations in biometric data. As a result, a trade-off exists between the key length and matching accuracy. Moreover, these systems are vulnerable to privacy leakage, i.e., it is trivial to recover the original biometric template given the secure sketch and its associated cryptographic key. In this work, we propose a novel key-binding biometric cryptosystem framework, referred to as Cancelable Biometrics Vault (CBV), to address the above two limitations. The CBV framework is inspired by the cryptographic principle of chaffing and winnowing. It utilizes the concept of cancelable biometrics (CB) to generate secure biometric templates, which in turn are used to encode bits in a cryptographic key. While the CBV framework is generic and does not rely on a specific biometric representation, it does assume the availability of a suitable (satisfying the requirements of accuracy preservation, non-invertibility, and non-linkability) CB scheme for the given representation. To demonstrate the usefulness of the proposed CBV framework, we implement this approach using an extended BioEncoding scheme, which is a CB scheme appropriate for bit strings such as iris-codes. Unlike the baseline BioEncoding scheme, the extended version proposed in this work fulfills all the three requirements of a CB construct. Experiments show that the decoding accuracy of the proposed CBV framework is comparable to the recognition accuracy of the underlying CB construct, namely, the extended BioEncoding scheme, regardless of the cryptographic key size.

In this paper, we propose the use of Optical Coherence Tomography (OCT) imaging for the problem of iris presentation attack (PA) detection. We assess its viability by comparing its performance with respect to traditional iris imaging modalities, viz., near-infrared (NIR) and visible spectrum. OCT imaging provides a cross-sectional view of an eye, whereas traditional imaging provides 2D iris textural information. PA detection is performed using three state-of-the-art deep architectures (VGG19, ResNet50 and DenseNet121) to differentiate between bonafide and PA samples for each of the three imaging modalities. Experiments are performed on a dataset of 2,169 bonafide, 177 Van Dyke eyes and 360 cosmetic contact images acquired using all three imaging modalities under intra-attack (known PAs) and cross-attack (unknown PAs) scenarios. We observe promising results demonstrating OCT as a viable solution for iris presentation attack detection.

A face identification system compares an unknown input probe image to a gallery of labeled face images in order to determine the identity of the probe image. The result of identification is a ranked match list with the most similar gallery face image at the top (rank 1) and the least similar gallery face image at the bottom. In many systems, the top ranked gallery images may look very similar to the probe image as well as to each other and can sometimes result in the misidentification of the probe image. Such similar looking faces pertaining to different identities are referred to as lookalike faces. We hypothesize that a matcher specifically trained to disambiguate lookalike face images when combined with a regular face matcher will improve overall identification performance. This work proposes reranking the initial ranked match list using a disambiguator especially for lookalike face pairs. This work also evaluates schemes to select gallery images in the initial ranked match list that should be re- ranked. Experiments on the challenging TinyFace dataset shows that the proposed approach improves the closed-set identification accuracy of a state-of-the-art face matcher.

A text-independent speaker recognition system relies on successfully encoding speech factors such as vocal pitch, intensity, and timbre to achieve good performance. A majority of such systems are trained and evaluated using spoken voice or everyday conversational voice data. Spoken voice, however, exhibits a limited range of possible speaker dynamics, thus constraining the utility of the derived speaker recognition models. Singing voice, on the other hand, covers a broader range of vocal and ambient factors and can, therefore, be used to evaluate the robustness of a speaker recognition system. However, a majority of existing speaker recognition datasets only focus on the spoken voice. In comparison, there is a significant shortage of labeled singing voice data suitable for speaker recognition research. To address this issue, we assemble \textit{JukeBox} - a speaker recognition dataset with multilingual singing voice audio annotated with singer identity, gender, and language labels. We use the current state-of-the-art methods to demonstrate the difficulty of performing speaker recognition on singing voice using models trained on spoken voice alone. We also evaluate the effect of gender and language on speaker recognition performance, both in spoken and singing voice data. The complete \textit{JukeBox} dataset can be accessed at http://iprobe.cse.msu.edu/datasets/jukebox.html.

Most automated fingerprint recognition systems use minutiae points for comparing fingerprints. In the parlance of Computer Vision, minutiae can be viewed as handcrafted features, i.e., features that have been proposed by human experts for the task of fingerprint recognition. In this work, we raise the following question: Can a machine learning system automatically determine the significance of minutiae points for fingerprint matching? To this effect, a patch-based Siamese Convolutional Neural Network (CNN), which does not explicitly rely on the extraction of minutiae points, is designed and trained from scratch. The purpose of this network is to learn the most effective features for matching fingerprint images. The features learned by this network are analyzed using Gradient-weighted Class Activation Mapping (Grad-CAM) to determine if they correlate with the locations of minutiae points. Our experiments suggest that the proposed network automatically learns to focus on minutiae points, when available, for fingerprint matching. Thus, an automated learner without any explicit domain knowledge establishes the significance of minutiae points for fingerprint matching.

Iris based recognition systems are vulnerable to presentation attacks (PAs) where artifacts such as cosmetic contact lenses, artificial eyes and printed eyes can be used to fool the system. While many learning-based algorithms have been proposed to detect such attacks, very few are equipped to handle previously unseen or newly constructed PAs. In this research, we propose a presentation attack detection (PAD) method that utilizes a discriminator that is trained to distinguish between bonafide iris images and synthetically generated iris images. We hypothesize that such a discriminator will generate a tight boundary around the bonafide samples. This would allow the discriminator to better separate the bonafide samples from all types of PA samples. For generating synthetic irides, we train the Relativistic Average Standard Generative Adversarial Network (RaSGAN) that has been shown to generate higher resolution and better quality images than standard GANs. The relativistic discriminator (RD) component of the trained RaS-GAN is then appropriated for PA detection and is referred to as RD-PAD. Experimental results convey the efficacy of the RD-PAD as a one-class anomaly detector.

Speaker recognition algorithms are negatively impacted by the quality of the input speech signal. In this work, we approach the problem of speaker recognition from severely degraded audio data by judiciously combining two commonly used features: Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC). Our hypothesis rests on the observation that MFCC and LPC capture two distinct aspects of speech, viz., speech perception and speech production. A carefully crafted 1D Triplet Convolutional Neural Network (1D-Triplet-CNN) is used to combine these two features in a novel manner, thereby enhancing the performance of speaker recognition in challenging scenarios. Extensive evaluation on multiple datasets, different types of audio degradations, multi-lingual speech, varying length of audio samples, etc. convey the efficacy of the proposed approach over existing speaker recognition methods, including those based on iVector and xVector.

Recent research has established the possibility of deducing soft-biometric attributes such as age, gender and race from an individual's face image with high accuracy. However, this raises privacy concerns, especially when face images collected for biometric recognition purposes are used for attribute analysis without the person's consent. To address this problem, we develop a technique for imparting soft biometric privacy to face images via an image perturbation methodology. The image perturbation is undertaken using a GAN-based Semi-Adversarial Network (SAN) - referred to as PrivacyNet - that modifies an input face image such that it can be used by a face matcher for matching purposes but cannot be reliably used by an attribute classifier. Further, PrivacyNet allows a person to choose specific attributes that have to be obfuscated in the input face images (e.g., age and race), while allowing for other types of attributes to be extracted (e.g., gender). Extensive experiments using multiple face matchers, multiple age/gender/race classifiers, and multiple face datasets demonstrate the generalizability of the proposed multi-attribute privacy enhancing method across multiple face and attribute classifiers.

Designing face recognition systems that are capable of matching face images obtained in the thermal spectrum with those obtained in the visible spectrum is a challenging problem. In this work, we propose the use of semantic-guided generative adversarial network (SG-GAN) to automatically synthesize visible face images from their thermal counterparts. Specifically, semantic labels, extracted by a face parsing network, are used to compute a semantic loss function to regularize the adversarial network during training. These semantic cues denote high-level facial component information associated with each pixel. Further, an identity extraction network is leveraged to generate multi-scale features to compute an identity loss function. To achieve photo-realistic results, a perceptual loss function is introduced during network training to ensure that the synthesized visible face is perceptually similar to the target visible face image. We extensively evaluate the benefits of individual loss functions, and combine them effectively to learn the mapping from thermal to visible face images. Experiments involving two multispectral face datasets show that the proposed method achieves promising results in both face synthesis and cross-spectral face matching.

Reliable anticipation of pedestrian trajectory is imperative for the operation of autonomous vehicles and can significantly enhance the functionality of advanced driver assistance systems. While significant progress has been made in the field of pedestrian detection, forecasting pedestrian trajectories remains a challenging problem due to the unpredictable nature of pedestrians and the huge space of potentially useful features. In this work, we present a deep learning approach for pedestrian trajectory forecasting using a single vehicle-mounted camera. Deep learning models that have revolutionized other areas in computer vision have seen limited application to trajectory forecasting, in part due to the lack of richly annotated training data. We address the lack of training data by introducing a scalable machine annotation scheme that enables our model to be trained using a large dataset without human annotation. In addition, we propose Dynamic Trajectory Predictor (DTP), a model for forecasting pedestrian trajectory up to one second into the future. DTP is trained using both human and machine-annotated data, and anticipates dynamic motion that is not captured by linear models. Experimental evaluation confirms the benefits of the proposed model.

Indoor video surveillance systems often use the face modality to establish the identity of a person of interest. However, the face image may not offer sufficient discriminatory information in many scenarios due to substantial variations in pose, illumination, expression, resolution and distance between the subject and the camera. In such cases, the inclusion of an additional biometric modality can benefit the recognition process. In this regard, we consider the fusion of voice and face modalities for enhancing the recognition accuracy. The main contribution of this work is assembling a multimodal (face and voice), semi-constrained, indoor video surveillance dataset referred to as the MSU Audio-Video Indoor Surveillance (MSU-AVIS) dataset. We use a consumer-grade camera with a built-in microphone to acquire data for this purpose. We use current state-of-art deep-learning based methods to perform face and speaker recognition on the collected dataset for establishing baseline performance. We also explore multiple fusion schemes to combine face and speaker recognition to perform effective person recognition on audio-video surveillance data. Experiments convey the efficacy of the proposed multimodal fusion scheme (face and voice) over unimodal approaches in surveillance scenarios. The collected dataset is being made available for research purposes.

In this paper, we present Auto-Tuned Models, or ATM, a distributed, collaborative, scalable system for automated machine learning. Users of ATM can simply upload a dataset, choose a subset of modeling methods, and choose to use ATM's hybrid Bayesian and multi-armed bandit optimization system. The distributed system works in a load-balanced fashion to quickly deliver results in the form of ready-to-predict models, confusion matrices, cross-validation results, and training timings. By automating hyperparameter tuning and model selection, ATM returns the emphasis of the machine learning workflow to its most irreducible part: feature engineering. We demonstrate the usefulness of ATM on 420 datasets from OpenML and train over 3 million classifiers. Our initial results show ATM can beat human-generated solutions for 30% of the datasets, and can do so in 1/100th of the time.

Iris recognition entails the use of iris images to recognize an individual. In some cases, the iris image acquired from an individual can be modified by subjecting it to successive photometric transformations such as brightening, gamma correction, median filtering and Gaussian smoothing, resulting in a family of transformed images. Automatically inferring the relationship between the set of transformed images is important in the context of digital image forensics. In this regard, we develop a method to generate an Image Phylogeny Tree (IPT) from a set of such transformed images. Our strategy entails modeling an arbitrary photometric transformation as a linear or non-linear function and utilizing the parameters of the model to quantify the relationship between pairs of images. The estimated parameters are then used to generate the IPT. Modest, yet promising, results are obtained in terms of parameter estimation and IPT generation.

We present a deep learning based algorithm for speaker recognition from degraded audio signals. We use the commonly employed Mel-Frequency Cepstral Coefficients (MFCC) for representing the audio signals. A convolutional neural network (CNN) based on 1D filters, rather than 2D filters, is then designed. The filters in the CNN are designed to learn inter-dependency between cepstral coefficients extracted from audio frames of fixed temporal expanse. Our approach aims at extracting speaker dependent features, like Sub-glottal and Supra-glottal features, of the human speech production apparatus for identifying speakers from degraded audio signals. The performance of the proposed method is compared against existing baseline schemes on both synthetically and naturally corrupted speech data. Experiments convey the efficacy of the proposed architecture for speaker recognition.

Traditionally, the accuracy of signature verification systems has been evaluated following a protocol that considers two independent impostor scenarios: random forgeries and skilled forgeries. Although such an approach is not necessarily incorrect, it can lead to a misinterpretation of the results of the assessment process. Furthermore, such a full separation between both types of impostors may be unrealistic in many operational real-world applications. The current article discusses the soundness of the random-skilled impostor dichotomy and proposes complementary approaches to report the accuracy of signature verification systems, discussing their advantages and limitations.

While the primary purpose for collecting biometric data (such as face images, iris, fingerprints, etc.) is for person recognition, yet recent advances in machine learning has shown the possibility of extracting auxiliary information from biometric data such as age, gender, health attributes, etc. These auxiliary attributes are sometimes referred to as soft biometrics. This automatic extraction of soft biometric attributes can happen without the user's agreement, thereby raising several privacy concerns. In this work, we design a technique that modifies a face image such that its gender as assessed by a gender classifier is perturbed, while its biometric utility as assessed by a face matcher is retained. Given an arbitrary biometric matcher and an attribute classifier, the proposed method systematically perturbs the input image such that the output of the attribute classifier is confounded, while the output of the biometric matcher is not significantly impacted. Experimental analysis convey the efficacy of the scheme in imparting gender privacy to face images.

This paper investigates the security of partial fingerprint-based authentication systems, especially when multiple fingerprints of a user are enrolled. A number of consumer electronic devices, such as smartphones, are beginning to incorporate fingerprint sensors for user authentication. The sensors embedded in these devices are generally small and the resulting images are, therefore, limited in size. To compensate for the limited size, these devices often acquire multiple partial impressions of a single finger during enrollment to ensure that at least one of them will successfully match with the image obtained from the user during authentication. Furthermore, in some cases, the user is allowed to enroll multiple fingers, and the impressions pertaining to multiple partial fingers are associated with the same identity (i.e., one user). A user is said to be successfully authenticated if the partial fingerprint obtained during authentication matches any one of the stored templates. This paper investigates the possibility of generating a “MasterPrint,” a synthetic or real partial fingerprint that serendipitously matches one or more of the stored templates for a significant number of users. Our preliminary results on an optical fingerprint data set and a capacitive fingerprint data set indicate that it is indeed possible to locate or generate partial fingerprints that can be used to impersonate a large number of users. In this regard, we expose a potential vulnerability of partial fingerprint-based authentication systems, especially when multiple impressions are enrolled per finger.

A supervised learning system requires labeled data during the training phase. Obtaining labels can be an expensive process, especially in medical imaging applications where a qualified expert may be needed to carefully analyze images and annotate them. This constrains the amount of labeled data available. This study explores the possibility of incorporating labeling behavior (viz., labeling latency) in a supervised convolutional neural network (CNN) framework in order to improve its performance in the presence of limited labeled data. The problem of “spot” detection in MRI scans is considered in this work. In this two-class problem, (a) labeling behavior is available only during the training phase unlike traditional features that are available both during training and testing; and (b) the labeling behavior is associated with only one class (the positive samples) unlike other side information that is available for all classes. To address these issues, a new CNN architecture referred to as L-CNN is designed. The proposed method utilizes the labeling behavior of the expert to cluster the labeled data into multiple categories; a source CNN is then trained to distinguish between these categories. Next, a transfer learning paradigm is used where a target CNN is initialized using this source CNN and its weights updated with the limited labeled data that is available. Experimental results on an existing MRI database show that the proposed L-CNN performs better than a conventional CNN and, further, significantly outperforms the previous state-of-the-art, thereby establishing a new baseline for “spot” detection in MRI.

Vascular Similarly Measurement (VSM) is an important tool in many biomedical applications. However, designing a robust computational VSM remains a challenge. We investigate different wavelet families and their orders to find their efficacy as feature extractors for computational VSM. Using a 50-subject dataset of RGB ocular surface vasculature images, we show that a compact feature vector composed of wavelet packet energies derived from Db1 wavelets, in conjunction with Fisher linear discriminant analysis and judged by the ensuing ROCs, is best suited for this task. Coif1 and Rbio2.4 were found to be the next best two wavelets for this purpose. Repetition of the same experiments using neural networks confirmed the optimality of the above suite of features for VSM.

Presents a listing of the new Associate Editors for this issue of the publication.

Recent research has explored the possibility of extracting ancillary information from primary biometric traits viz., face, fingerprints, hand geometry, and iris. This ancillary information includes personal attributes, such as gender, age, ethnicity, hair color, height, weight, and so on. Such attributes are known as soft biometrics and have applications in surveillance and indexing biometric databases. These attributes can be used in a fusion framework to improve the matching accuracy of a primary biometric system (e.g., fusing face with gender information), or can be used to generate qualitative descriptions of an individual (e.g., young Asian female with dark eyes and brown hair). The latter is particularly useful in bridging the semantic gap between human and machine descriptions of the biometric data. In this paper, we provide an overview of soft biometrics and discuss some of the techniques that have been proposed to extract them from the image and the video data. We also introduce a taxonomy for organizing and classifying soft biometric attributes, and enumerate the strengths and limitations of these attributes in the context of an operational biometric system. Finally, we discuss open research problems in this field. This survey is intended for researchers and practitioners in the field of biometrics.

While biometrics technology has created new solutions to person authentication and has evolved to play a critical role in personal, national, and global security, the potential for the technology to be fooled orspoofedis now widely acknowledged. For example, fingerprint verification systems can be spoofed with a synthetic material, such as gelatine, inscribed with the fingerprint ridges of an enrolled individual. Iris and face recognition systems are vulnerable to printed photographs or video sequences of an enrolled user’s eye or face. Speaker recognition systems can be spoofed through the use of replayed, synthesized, or converted speech.

A fingerprint spoof detector is a pattern classifier that is used to distinguish a live finger from a fake (spoof) one in the context of an automated fingerprint recognition system. Most spoof detectors are learning-based and rely on a set of training images. Consequently, the performance of any such spoof detector significantly degrades when encountering spoofs fabricated using novel materials not found in the training set. In real-world applications, the problem of fingerprint spoof detection must be treated as an open set recognition problem where incomplete knowledge of the fabrication materials used to generate spoofs is present at training time, and novel materials may be encountered during system deployment. To mitigate the security risk posed by novel spoofs, this paper introduces: 1) the use of the Weibull-calibrated SVM (W-SVM), which is relatively robust for open set recognition, as a novel-material detector and a spoof detector and 2) a scheme for the automatic adaptation of the W-SVM-based spoof detector to new spoof materials that leverages interoperability across classifiers. Experiments conducted on new partitions of the LivDet 2011 database designed for open set evaluation suggest: 1) a 97% increase in the error rate of the existing spoof detectors when tested using new spoof materials and 2) up to 44% improvement in spoof detection performance across spoof materials when the proposed adaptive approach is used.

The goal of this paper is to determine whether keystroke sound can be used to recognize a user. In this regard, we analyze the discriminative power of keystroke sound in the context of a continuous user authentication application. Motivated by the concept of digraphs used in modeling keystroke dynamics, a virtual alphabet is first learned from keystroke sound segments. Next, the digraph latency within the pairs of virtual letters, along with other statistical features, is used to generate match scores. The resultant scores are indicative of the similarities between two sound streams, and are fused to make a final authentication decision. Experiments on both static text-based and free text-based authentications on a database of 50 subjects demonstrate the potential as well as the limitations of keystroke sound.

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.

A fingerprint liveness detector is a pattern classifier that is used to distinguish a live finger from a fake (spoof) one in the context of an automated fingerprint recognition system. Most liveness detectors are learning-based and rely on a set of training images. Consequently, the performance of a liveness detector significantly degrades upon encountering spoofs fabricated using new materials not used during the training stage. To mitigate the security risk posed by new spoofs, it is necessary to automatically adapt the liveness detector to new spoofing materials. The aim of this work is to design a scheme for automatic adaptation of a liveness detector to novel spoof materials encountered during the operational phase. To facilitate this, a novel-material detector is used to flag input images that are deemed to be made of a new spoofing material. Such flagged images are then used to retrain the liveness detector. Experiments conducted on the LivDet 2011 database suggest (i) a 62% increase in the error rate of existing liveness detectors when tested using new spoof materials, and (ii) upto 46% improvement in liveness detection performance across spoof materials when the proposed adaptive approach is used.

We consider the problem of matching face against iris images using ocular information. In biometrics, face and iris images are typically acquired using sensors operating in visible (VIS) and near-infrared (NIR) spectra, respectively. This presents a challenging problem of matching images corresponding to different biometric modalities, imaging spectra, and spatial resolutions. We propose the usage of ocular traits that are common between face and iris images (viz., iris and ocular region) to perform matching. Iris matching is performed using a commercial software, while ocular regions are matched using three different techniques: Local Binary Patterns (LBP), Normalized Gradient Correlation (NGC), and Joint Dictionary-based Sparse Representation (JDSR). Experimental results on a database containing 1358 images of 704 subjects indicate that ocular region can provide better performance than iris biometric under a challenging cross-modality matching scenario.

Fingerprint liveness detection algorithms have been used to disambiguate live fingerprint samples from spoof (fake) fingerprints fabricated using materials such as latex, gelatine, etc. Most liveness detection algorithms are learning-based and dependent on the fabrication materials used to generate spoofs during the training stage. Consequently, the performance of a liveness detector is significantly degraded upon encountering fabrication materials that were not used during the training stage. The aim of this work is to design a simple pre-processing scheme that can improve the interoperability of liveness detectors across different fabrication materials - including those not observed during the training stage. Such a generalization ability is desirable in liveness detectors. Experiments on the LivDet 2011 fake fingerprint dataset suggest that (a) different fabrication materials when used in the training stage impart different degrees of generalization ability to the liveness detector and (b) the proposed pre-processing scheme improves generalization performance by upto 44%.

Research in automated human gait recognition has largely focused on developing robust feature representation and matching algorithms. In this paper, we investigate the possibility of clustering gait patterns based on the features extracted by automated gait matchers. In this regard, a k-means based clustering approach is used to categorize the feature sets extracted by three different gait matchers. Experiments are conducted in order to determine if (a) the clusters of identities corresponding to the three matchers are similar, and (b) if there is a correlation between gait patterns within each cluster and physical attributes such as gender, body area, height, stride, and cadence. Results demonstrate that human gait patterns can be clustered, where each cluster is defined by identities sharing similar physical attributes. In particular, body area and gender are found to be the primary attributes captured by gait matchers to assess similarity between gait patterns. However, the strength of the correlation between clusters and physical attributes is different across the three matchers, suggesting that gait matchers "weight" attributes differently. The results of this study should be of interest to gait recognition and identification-at-a-distance researchers.

This work explores the possibility of mixing two different fingerprints, pertaining to two different fingers, at the image level in order to generate a new fingerprint. To mix two fingerprints, each fingerprint pattern is decomposed into two different components, viz., the continuous and spiral components. After prealigning the components of each fingerprint, the continuous component of one fingerprint is combined with the spiral component of the other fingerprint. Experiments on the West Virginia University (WVU) and FVC2002 datasets show that mixing fingerprints has several benefits: (a) it can be used to generate virtual identities from two different fingers; (b) it can be used to obscure the information present in an individual's fingerprint image prior to storing it in a central database; and (c) it can be used to generate a cancelable fingerprint template, i.e., the template can be reset if the mixed fingerprint is compromised.

In a biometric identification system, the identity corresponding to the input data (probe) is typically determined by comparing it against the templates of all identities in a database (gallery). Exhaustive matching against a large number of identities increases the response time of the system and may also reduce the accuracy of identification. One way to reduce the response time is by designing biometric templates that allow for rapid matching, as in the case of IrisCodes. An alternative approach is to limit the number of identities against which matching is performed based on criteria that are fast to evaluate. We propose a method for generating fixed-length codes for indexing biometric databases. An index code is constructed by computing match scores between a biometric image and a fixed set of reference images. Candidate identities are retrieved based on the similarity between the index code of the probe image and those of the identities in the database. The proposed technique can be easily extended to retrieve pertinent identities from multimodal databases. Experiments on a chimeric face and fingerprint bimodal database resulted in an 84% average reduction in the search space at a hit rate of 100%. These results suggest that the proposed indexing scheme has the potential to substantially reduce the response time without compromising the accuracy of identification.

In an operational biometric verification system, changes in biometric data over a period of time can affect the classification accuracy. Online learning has been used for updating the classifier decision boundary. However, this requires labeled data that is only available during new enrolments. This paper presents a biometric classifier update algorithm in which the classifier decision boundary is updated using both labeled enrolment instances and unlabeled probe in- stances. The proposed co-training online classifier update algorithm is presented as a semi-supervised learning task and is applied to a face verification application. Experiments indicate that the proposed algorithm improves the performance both in terms of classification accuracy and computational time.

Multibiometric systems fuse the evidence (e.g., match scores) pertaining to multiple biometric modalities or classifiers. Most score-level fusion schemes discussed in the literature require the processing (i.e., feature extraction and matching) of every modality prior to invoking the fusion scheme. This paper presents a framework for dynamic classifier selection and fusion based on the quality of the gallery and probe images associated with each modality with multiple classifiers. The quality assessment algorithm for each biometric modality computes a quality vector for the gallery and probe images that is used for classifier selection. These vectors are used to train Support Vector Machines (SVMs) for decision making. In the proposed framework, the bio- metric modalities are arranged sequentially such that the stronger biometric modality has higher priority for being processed. Since fusion is required only when all unimodal classifiers are rejected by the SVM classifiers, the average computational time of the proposed framework is significantly reduced. Experimental results on different multi-modal databases involving face and fingerprint show that the proposed quality-based classifier selection framework yields good performance even when the quality of the bio- metric sample is sub-optimal.

We investigate the question of whether facial metrology can be exploited for reliable gender prediction. A new method based solely on metrological information from facial landmarks is developed. Here, metrological features are defined in terms of specially normalized angle and distance measures and computed based on given landmarks on facial images. The performance of the proposed metrology- based method is compared with that of a state-of-the-art appearance-based method for gender classification. Results are reported on two standard face databases, namely, MUCT and XM2VTS containing 276 and 295 images, respectively. The performance of the metrology-based approach was slightly lower than that of the appearance- based method by only about 3.8% for the MUCT database and about 5.7% for the XM2VTS database.

Automatic gender classification based on face images is receiving increased attention in the biometrics community. Most gender classification systems have been evaluated only on face images captured in the visible spectrum. In this work, the possibility of deducing gender from face images obtained in the near-infrared (NIR) and thermal (THM) spectra is established. It is observed that the use of local binary pattern histogram (LBPH) features along with discriminative classifiers results in reasonable gender classification accuracy in both the NIR and THM spectra. Further, the performance of human subjects in classifying thermal face images is studied. Experiments suggest that machine-learning methods are better suited than humans for gender classification from face images in the thermal spectrum.

Ocular biometrics has made significant progress over the past decade primarily due to advances in iris recognition. Initial research in the field of iris recognition focused on the acquisition and processing of frontal irides which may require considerable subject cooperation. However, when the iris is off-angle with respect to the acquisition device, the sclera (the white part of the eye) is exposed. The sclera is covered by a thin transparent layer called conjunctiva. Both the episclera and conjunctiva contain blood vessels that are observable from the outside. In this work, these blood vessels are referred to as conjunctival vasculature. Iris patterns are better observed in the near infrared spectrum while conjunctival vasculature is better seen in the visible spectrum. Therefore, multispectral (i.e., color-infrared) images of the eye are acquired to allow for the combination of the iris biometric with the conjunctival vasculature. The paper focuses on conjunctival vasculature enhancement, registration and matching. Initial results are promising and suggest the need for further investigation of this biometric in a bimodal configuration with iris.

The focus of this work is on improving the recognition performance of low-resolution iris video frames acquired under varying illumination. To facilitate this, an image-level fusion scheme with modest computational requirements is proposed. The proposed algorithm uses the evidence of multiple image frames of the same iris to extract discriminatory information via the Principal Components Transform (PCT). Experimental results on a subset of the MBGC NIR iris database demonstrate the utility of this scheme to achieve improved recognition accuracy when low-resolution probe images are compared against high-resolution gallery images.

We study the problem of restoring severely degraded face images such as images scanned from passport photos or images subjected to fax compression, downscaling, and printing. The purpose of this paper is to illustrate the complexity of face recognition in such realistic scenarios and to provide a viable solution to it. The contributions of this work are two-fold. First, a database of face images is assembled and used to illustrate the challenges associated with matching severely degraded face images. Second, a preprocessing scheme with low computational complexity is developed in order to eliminate the noise present in degraded images and restore their quality. An extensive experimental study is performed to establish that the proposed restoration scheme improves the quality of the ensuing face images while simultaneously improving the performance of face matching.

The term periocular refers to the facial region in the immediate vicinity of the eye. Acquisition of the periocular biometric is expected to require less subject cooperation while permitting a larger depth of field compared to traditional ocular biometric traits (viz., iris, retina, and sclera). In this work, we study the feasibility of using the periocular region as a biometric trait. Global and local information are extracted from the periocular region using texture and point operators resulting in a feature set for representing and matching this region. A number of aspects are studied in this work, including the 1) effectiveness of incorporating the eyebrows, 2) use of side information (left or right) in matching, 3) manual versus automatic segmentation schemes, 4) local versus global feature extraction schemes, 5) fusion of face and periocular biometrics, 6) use of the periocular biometric in partially occluded face images, 7) effect of disguising the eyebrows, 8) effect of pose variation and occlusion, 9) effect of masking the iris and eye region, and 10) effect of template aging on matching performance. Experimental results show a rank-one recognition accuracy of 87.32% using 1136 probe and 1136 gallery periocular images taken from 568 different subjects (2 images/subject) in the Face Recognition Grand Challenge (version 2.0) database with the fusion of three different matchers.

Preserving the privacy of digital biometric data (e.g., face images) stored in a central database has become of paramount importance. This work explores the possibility of using visual cryptography for imparting privacy to biometric data such as fingerprint images, iris codes, and face images. In the case of faces, a private face image is dithered into two host face images (known as sheets) that are stored in two separate database servers such that the private image can be revealed only when both sheets are simultaneously available; at the same time, the individual sheet images do not reveal the identity of the private image. A series of experiments on the XM2VTS and IMM face databases confirm the following: 1) the possibility of hiding a private face image in two host face images; 2) the successful matching of face images reconstructed from the sheets; 3) the inability of sheets to reveal the identity of the private face image; 4) using different pairs of host images to encrypt different samples of the same private face; and 5) the difficulty of cross-database matching for determining identities. A similar process is used to de-identify fingerprint images and iris codes prior to storing them in a central database.

The problem of face verification across the short wave infrared spectrum (SWIR) is studied in order to illustrate the advantages and limitations of SWIR face verification. The contributions of this work are two-fold. First, a database of 50 subjects is assembled and used to illustrate the challenges associated with the problem. Second, a set of experiments is performed in order to demonstrate the possibility of SWIR cross-spectral matching. Experiments also show that images captured under different SWIR wavelengths can be matched to visible images with promising results. The role of multispectral fusion in improving recognition performance in SWIR images is finally illustrated. To the best of our knowledge, this is the first time cross-spectral SWIR face recognition is being investigated in the open literature.

The widespread deployment of Automated Fingerprint Identification Systems (AFIS) in law enforcement and border control applications has prompted some individuals with criminal background to evade identification by purposely altering their fingerprints. Available fingerprint quality assessment software cannot detect most of the altered fingerprints since the implicit image quality does not always degrade due to alteration. In this paper, we classify the alterations observed in an operational database into three categories and propose an algorithm to detect altered fingerprints. Experiments were conducted on both real-world altered fingerprints and synthetically generated altered fingerprints. At a false alarm rate of 7%, the proposed algorithm detected 92% of the altered fingerprints, while a well-known fingerprint quality software, NFIQ, only detected 20% of the altered fingerprints.

Most iris recognition systems use the global and local texture information of the iris in order to recognize individuals. In this work, we investigate the use of macro-features that are visible on the anterior surface of RGB images of the iris for matching and retrieval. These macro-features correspond to structures such as moles, freckles, nevi, melanoma, etc. and may not be present in all iris images. Given an image of a macro-feature, the goal is to determine if it can be used to successfully retrieve the associated iris from the database. To address this problem, we use features extracted by the Scale-Invariant Feature Transform (SIFT) to represent and match macro-features. Experiments using a subset of 770 distinct irides from the Miles Research Iris Database suggest the possibility of using macro-features for iris characterization and retrieval.

We propose a quality-based fusion scheme for improving the recognition accuracy using color iris images characterized by three spectral channels - Red, Green and Blue. In the proposed method, quality scores are employed to select two channels of a color iris image which are fused at the image level using a Redundant Discrete Wavelet Transform (RDWT). The fused image is then used in a score-level fusion framework along with the remaining channel to improve recognition accuracy. Experimental results on a heterogenous color iris database demonstrate the efficacy of the technique when compared against other score-level and image-level fusion methods. The proposed method can potentially benefit the use of color iris images in conjunction with their NIR counterparts.

Human recognition based on the iris biometric is severely impacted when encountering non-ideal images of the eye characterized by occluded irises, motion and spatial blur, poor contrast, and illumination artifacts. This paper discusses the use of the periocular region surrounding the iris, along with the iris texture patterns, in order to improve the overall recognition performance in such images. Periocular texture is extracted from a small, fixed region of the skin surrounding the eye. Experiments on the images extracted from the Near Infra-Red (NIR) face videos of the Multi Biometric Grand Challenge (MBGC) dataset demonstrate that valuable information is contained in the periocular region and it can be fused with the iris texture to improve the overall identification accuracy in non-ideal situations.

Biometric fusion consolidates the output of multiple biometric classifiers to render a decision about the identity of an individual. We consider the problem of designing a fusion scheme when 1) the number of training samples is limited, thereby affecting the use of a purely density-based scheme and the likelihood ratio test statistic; 2) the output of multiple matchers yields conflicting results; and 3) the use of a single fusion rule may not be practical due to the diversity of scenarios encountered in the probe dataset. To address these issues, a dynamic reconciliation scheme for fusion rule selection is proposed. In this regard, the contribution of this paper is two-fold: 1) the design of a sequential fusion technique that uses the likelihood ratio test-statistic in conjunction with a support vector machine classifier to account for errors in the former; and 2) the design of a dynamic selection algorithm that unifies the constituent classifiers and fusion schemes in order to optimize both verification accuracy and computational cost. The case study in multiclassifier face recognition suggests that the proposed algorithm can address the issues listed above. Indeed, it is observed that the proposed method performs well even in the presence of confounding covariate factors thereby indicating its potential for large-scale face recognition.

Automating the process of postmortem identification of deceased individuals based on dental characteristics is receiving increased attention. With the large number of victims encountered in mass disasters (e.g., Asian tsunami), automating the identification process would enhance the scalability of this biometric. However, archiving and retrieving dental records from large databases is a challenging task and has received inadequate attention in the literature. This paper concerns itself with the task of efficient fast retrieving dental records from a database in order to assist the forensic expert in identifying deceased individuals in a rapid manner. The proposed method is an appearance-based technique that consolidates the evidence presented by individual teeth in a dental record, i.e., it `moves' from tooth-to-tooth in order to render a record-to-record matching score. The proposed method is shown to reduce the searching time of record-to-record matching by a factor of hundred. Experimental results indicate that the proposed approach requires significantly less time compared to the other approaches suggested in the literature thereby underscoring its relevance in real-time applications.

Performing face detection and identification in low-resolution videos (e.g., surveillance videos) is a challenging task. The task entails extracting an unknown face image from the video and comparing it against identities in the gallery database. To facilitate biometric recognition in such videos, fusion techniques may be used to consolidate the facial information of an individual, available across successive low-resolution frames. For example, super-resolution schemes can be used to improve the spatial resolution of facial objects contained in these videos (image-level fusion). However, the output of the super-resolution routine can be significantly affected by large changes in facial pose in the constituent frames. To mitigate this concern, an adaptive frame selection technique is developed in this work. The proposed technique automatically disregards frames that can cause severe artifacts in the super-resolved output, by examining the optical flow matrices pertaining to successive frames. Experimental results demonstrate an improvement in the identification performance when the proposed technique is used to automatically select the input frames necessary for super-resolution. In addition, improvements in output image quality and computation time are observed. The paper also compares image-level fusion against score-level fusion where the low-resolution frames are first spatially interpolated and the simple sum rule is used to consolidate the match scores corresponding to the interpolated frames. On comparing the two fusion methods, it is observed that score-level fusion outperforms image-level fusion.

The richness and apparent stability of the iris texture make it a robust biometric trait for personal authentication. The performance of an automated iris recognition system is affected by the accuracy of the segmentation process used to localize the iris structure. Most segmentation models in the literature assume that the pupillary, limbic, and eyelid boundaries are circular or elliptical in shape. Hence, they focus on determining model parameters that best fit these hypotheses. However, it is difficult to segment iris images acquired under nonideal conditions using such conic models. In this paper, we describe a novel iris segmentation scheme employing geodesic active contours (GACs) to extract the iris from the surrounding structures. Since active contours can 1) assume any shape and 2) segment multiple objects simultaneously, they mitigate some of the concerns associated with traditional iris segmentation models. The proposed scheme elicits the iris texture in an iterative fashion and is guided by both local and global properties of the image. The matching accuracy of an iris recognition system is observed to improve upon application of the proposed segmentation algorithm. Experimental results on the CASIA v3.0 and WVU nonideal iris databases indicate the efficacy of the proposed technique.

Given a query iris image, the goal of indexing is to identify and retrieve a small subset of candidate irides from the database in order to determine a possible match. This can significantly improve the response time of iris recognition systems operating in the identification mode. In this work, we analyze two different approaches to iris indexing. The first technique is based on the analysis of IrisCodes (post-encoding indexing); the second technique is based on the analysis of features extracted from the iris texture (pre-encoding indexing). Experiments on a subset of the publicly available CASIA-IrisV3 database compare the two approaches and illustrate the potential of the proposed indexing methods for large scale iris identification.

Biometric sensor interoperability refers to the ability of a system to compensate for the variability introduced in the biometric data of an individual due to the deployment of different sensors. Poor intersensor performance has been reported in different biometric domains including fingerprint, face, iris, and speech. In the context of fingerprint technology, variations are observed in the acquired images of a fingerprint due to differences in sensor resolution, scanning area, sensing technology, etc., which subsequently impact the feature set extracted from these images. The inability of a fingerprint matcher to compensate for these variations introduced by different sensors results in inferior intersensor matching performance. In this work, a nonlinear calibration scheme based on the Thin- Plate Spline (TPS) model is used to register a pair of fingerprint sensors. The proposed calibration technique relies on the evidence of a few image pairs acquired using the two sensors to generate an average deformation model that defines the spatial relationship between the two sensors. This assists in the systematic perturbation of images/features pertaining to one sensor in order to make them compatible with images/features originating from the other sensor. Experimental results using multiple fingerprint data sets confirm the efficacy of the proposed method in addressing intersensor geometric variations.

Face recognition in video is being actively studied as a covert method of human identification in surveillance systems. Identifying human faces in video is a difficult problem due to the presence of large variations in facial pose and lighting, and poor image resolution. However, by taking advantage of the diversity of the information contained in video, the performance of a face recognition system can be enhanced. In this work we explore (a) the adaptive use of multiple face matchers in order to enhance the performance of face recognition in video, and (b) the possibility of appropriately populating the database (gallery) in order to succinctly capture intra class variations. To extract the dynamic information in video, the facial poses in various frames are explicitly estimated using active appearance model (AAM) and a factorization based 3D face reconstruction technique. We also estimate the motion blur using discrete cosine transformation (DCT). Our experimental results on 204 subjects in CMU's face-in-action (FIA) database show that the proposed recognition method provides consistent improvements in the matching performance using three different face matchers.

In an earlier work we had explored the possibility of utilizing the vascular pattern of the sclera, episclera, and conjunctiva as a biometric indicator. These blood vessels, which can be observed on the white part of the human eye, demonstrate rich and seemingly unique details in visible light, and can be easily imaged using commercially available digital cameras. In this work we discuss a new method to represent and match the textural intricacies of this vascular structure using wavelet-derived features in conjunction with neural network classifiers. Our experimental results, based on the evidence of 50 subjects, indicate the potential of the proposed scheme to characterize the individuality of the ocular surface vascular patterns and further confirm our assertion that these patterns are indeed unique across individuals.

In this paper we present clustering analysis of session-based Web workloads of eight Web servers using the intrasession characteristics (i.e., number of requests per session, session length in time, and bytes transferred per session) as variables. We use K-means algorithm and the Mahalanobis distance, and analyze the heavy-tailed behavior of intra-session characteristics and their correlations for each cluster. Our results show that clustering provides an efficient way to classify tens or hundreds thousands of sessions into several coherent classes that efficiently describe Web workloads. These classes reveal phenomena that cannot be observed when studying the workload as a whole.

Most fingerprint-based biometric systems store the minutiae template of a user in the database. It has been traditionally assumed that the minutiae template of a user does not reveal any information about the original fingerprint. In this paper, we challenge this notion and show that three levels of information about the parent fingerprint can be elicited from the minutiae template alone, viz., 1) the orientation field information, 2) the class or type information, and 3) the friction ridge structure. The orientation estimation algorithm determines the direction of local ridges using the evidence of minutiae triplets. The estimated orientation field, along with the given minutiae distribution, is then used to predict the class of the fingerprint. Finally, the ridge structure of the parent fingerprint is generated using streamlines that are based on the estimated orientation field. Line Integral Convolution is used to impart texture to the ensuing ridges, resulting in a ridge map resembling the parent fingerprint. The salient feature of this noniterative method to generate ridges is its ability to preserve the minutiae at specified locations in the reconstructed ridge map. Experiments using a commercial fingerprint matcher suggest that the reconstructed ridge structure bears close resemblance to the parent fingerprint.

Mosaicing entails the consolidation of information represented by multiple images through the application of a registration and blending procedure. We describe a face mosaicing scheme that generates a composite face image during enrollment based on the evidence provided by frontal and semiprofile face images of an individual. Face mosaicing obviates the need to store multiple face templates representing multiple poses of a user's face image. In the proposed scheme, the side profile images are aligned with the frontal image using a hierarchical registration algorithm that exploits neighborhood properties to determine the transformation relating the two images. Multiresolution splining is then used to blend the side profiles with the frontal image, thereby generating a composite face image of the user. A texture-based face recognition technique that is a slightly modified version of the C2 algorithm proposed by Serre et al. is used to compare a probe face image with the gallery face mosaic. Experiments conducted on three different databases indicate that face mosaicing, as described in this paper, offers significant benefits by accounting for the pose variations that are commonly observed in face images.

We propose a technique to create digital renditions of iris images that can be used to evaluate the performance of iris recognition algorithms. The proposed scheme is implemented in two stages. In the first stage, a Markov random field model is used to generate a background texture representing the global iris appearance. In the next stage a variety of iris features, viz., radial and concentric furrows, collarette and crypts, are generated and embedded in the texture field. The iris images synthesized in this manner are observed to bear close resemblance to real irises. Experiments confirm the potential of this scheme to generate a database of synthetic irises that can be used to evaluate iris recognition algorithms.

The performance of a fingerprint matching system is affected by the nonlinear deformation introduced in the fingerprint impression during image acquisition. This nonlinear deformation causes fingerprint features such as minutiae points and ridge curves to be distorted in a complex manner. A technique is presented to estimate the nonlinear distortion in fingerprint pairs based on ridge curve correspondences. The nonlinear distortion, represented using the thin-plate spline (TPS) function, aids in the estimation of an "average" deformation model for a specific finger when several impressions of that finger are available. The estimated average deformation is then utilized to distort the template fingerprint prior to matching it with an input fingerprint. The proposed deformation model based on ridge curves leads to a better alignment of two fingerprint images compared to a deformation model based on minutiae patterns. An index of deformation is proposed for selecting the "optimal" deformation model arising from multiple impressions associated with a finger. Results based on experimental data consisting of 1,600 fingerprints corresponding to 50 different fingers collected over a period of two weeks show that incorporating the proposed deformation model results in an improvement in the matching performance.

Establishing identity is becoming critical in our vastly interconnected society. Questions such as "Is she really who she claims to be?," "Is this person authorized to use this facility?," or "Is he in the watchlist posted by the government?" are routinely being posed in a variety of scenarios ranging from issuing a driver's license to gaining entry into a country. The need for reliable user authentication techniques has increased in the wake of heightened concerns about security and rapid advancements in networking, communication, and mobility. Biometrics, described as the science of recognizing an individual based on his or her physical or behavioral traits, is beginning to gain acceptance as a legitimate method for determining an individual's identity. Biometric systems have now been deployed in various commercial, civilian, and forensic applications as a means of establishing identity. In this paper, we provide an overview of biometrics and discuss some of the salient research issues that need to be addressed for making biometric technology an effective tool for providing information security. The primary contribution of this overview includes: 1) examining applications where biometric scan solve issues pertaining to information security; 2) enumerating the fundamental challenges encountered by biometric systems in real-world applications; and 3) discussing solutions to address the problems of scalability and security in large-scale authentication systems.

A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or, simply, biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics, it is possible to confirm or establish an individual's identity based on "who she is", rather than by "what she possesses" (e.g., an ID card) or "what she remembers" (e.g., a password). We give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.

It has been observed that the reduced contact area offered by solid-state fingerprint sensors does not provide sufficient information (e.g., number of minutiae) for high accuracy user verification. Further, multiple impressions of the same finger acquired by these sensors, may have only a small region of overlap thereby degrading the matching performance of the verification system. To deal with this problem, we have developed a fingerprint mosaicking scheme that constructs a composite fingerprint template using multiple impressions. A composite template reduces storage, improves matching time and alleviates the problem of template selection. In the proposed algorithm, two impressions (templates) of a finger are initially aligned using the corresponding minutiae points. This alignment is used by a modified version of the well-known iterative closest point algorithm (ICP) to compute a transformation matrix that defines the spatial relationship between the two impressions. The resulting transformation matrix is used in two ways: (a) the two templates are stitched together to generate a composite image. Minutiae points are then detected in this composite image; (b) the minutia maps obtained from each of the individual impressions are integrated to create a larger minutia map. Our experiments show that a composite template improves the performance of the fingerprint matching system by ∼ 4%.

Biometric systems that use a single biometric trait have to contend with noisy data, restricted degrees of freedom, failure-to-enroll problems, spoof attacks, and unacceptable error rates. Multibiometric systems that use multiple traits of an individual for authentication, alleviate some of these problems while improving verification performance. We demonstrate that the performance of multibiometric systems can be further improved by learning user-specific parameters. Two types of parameters are considered here. (i) Thresholds that are used to decide if a matching score indicates a genuine user or an impostor, and (ii) weights that are used to indicate the importance of matching scores output by each biometric trait. User-specific thresholds are computed using the cumulative histogram of impostor matching scores corresponding to each user. The user-specific weights associated with each biometric are estimated by searching for that set of weights which minimizes the total verification error. The tests were conducted on a database of 50 users who provided fingerprint, face and hand geometry data, with 10 of these users providing data over a period of two months. We observed that user-specific thresholds improved system performance by /spl sim/ 2%, while user-specific weights improved performance by /spl sim/ 3%.

The advent of solid-state fingerprint sensors presents a fresh challenge to traditional fingerprint matching algorithms. These sensors provide a small contact area (/spl ap/0.6"/spl times/0.6") for the fingertip and, therefore, sense only a limited portion of the fingerprint. Thus multiple impressions of the same fingerprint may have only a small region of overlap. Minutiae-based matching algorithms, which consider ridge activity only in the vicinity of minutiae points, are not likely to perform well on these images due to the insufficient number of corresponding points in the input and template images. We present a hybrid matching algorithm that uses both minutiae (point) information and texture (region) information for matching the fingerprints. Results obtained on the MSU-VERIDICOM database shows that a combination of the texture-based and minutiae-based matching scores leads to a substantial improvement in the overall matching performance.