Predicting a person's gender based on the iris texture has been explored by several researchers. This paper considers several dimensions of experimental work on this problem, including person-disjoint train and test, and the effect of cosmetics on eyelash occlusion and imperfect segmentation. We also consider the use of multi-layer perceptron and convolutional neural networks as classifiers, comparing the use of data-driven and hand-crafted features. Our results suggest that the gender-from-iris problem is more difficult than has so far been appreciated. Estimating accuracy using a mean of N person-disjoint train and test partitions, and considering the effect of makeup - a combination of experimental conditions not present in any previous work - we find a much weaker ability to predict gender-from-iris texture than has been suggested in previous work.
The extraction of ancillary information from biometric traits is known as . As defined by Dantcheva al @cite_14 , "[s]oft biometric traits are physical, behavioral, or material accessories, which are associated with an individual, and which can be useful for recognizing an individual."
@cite_14
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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. Facial attributes are emerging soft biometrics that have the potential to reject non-matches, for example, based on mismatching gender. To be usable in stand-alone systems, facial attributes must be extracted from images automatically and reliably. In this paper, we propose a simple yet effective solution for automatic facial attribute extraction by training a deep convolutional neural network (DCNN) for each facial attribute separately, without using any pre-training or dataset augmentation, and we obtain new state-of-the-art facial attribute classification results on the CelebA benchmark. To test the stability of the networks, we generated adversarial images -- formed by adding imperceptible non-random perturbations to original inputs which result in classification errors -- via a novel fast flipping attribute (FFA) technique. We show that FFA generates more adversarial examples than other related algorithms, and that DCNNs for certain attributes are generally robust to adversarial inputs, while DCNNs for other attributes are not. This result is surprising because no DCNNs tested to date have exhibited robustness to adversarial images without explicit augmentation in the training procedure to account for adversarial examples. Finally, we introduce the concept of natural adversarial samples, i.e., images that are misclassified but can be easily turned into correctly classified images by applying small perturbations. We demonstrate that natural adversarial samples commonly occur, even within the training set, and show that many of these images remain misclassified even with additional training epochs. This phenomenon is surprising because correcting the misclassification, particularly when guided by training data, should require only a small adjustment to the DCNN parameters. Biometric recognition refers to the automated recognition of individuals based on their biological and behavioral characteristics such as fingerprint, face, iris, and voice. The first scientific paper on automated fingerprint matching was published by Mitchell Trauring in the journal Nature in 1963. The first objective of this paper is to document the significant progress that has been achieved in the field of biometric recognition in the past 50 years since Trauring's landmark paper. This progress has enabled current state-of-the-art biometric systems to accurately recognize individuals based on biometric trait(s) acquired under controlled environmental conditions from cooperative users. Despite this progress, a number of challenging issues continue to inhibit the full potential of biometrics to automatically recognize humans. The second objective of this paper is to enlist such challenges, analyze the solutions proposed to overcome them, and highlight the research opportunities in this field. One of the foremost challenges is the design of robust algorithms for representing and matching biometric samples obtained from uncooperative subjects under unconstrained environmental conditions (e.g., recognizing faces in a crowd). In addition, fundamental questions such as the distinctiveness and persistence of biometric traits need greater attention. Problems related to the security of biometric data and robustness of the biometric system against spoofing and obfuscation attacks, also remain unsolved. Finally, larger system-level issues like usability, user privacy concerns, integration with the end application, and return on investment have not been adequately addressed. Unlocking the full potential of biometrics through inter-disciplinary research in the above areas will not only lead to widespread adoption of this promising technology, but will also result in wider user acceptance and societ al impact. Biometric recognition is an integral component of modern identity management and access control systems. Due to the strong and permanent link between individuals and their biometric traits, exposure of enrolled users? biometric information to adversaries can seriously compromise biometric system security and user privacy. Numerous techniques have been proposed for biometric template protection over the last 20 years. While these techniques are theoretically sound, they seldom guarantee the desired noninvertibility, revocability, and nonlinkability properties without significantly degrading the recognition performance. The objective of this work is to analyze the factors contributing to this performance divide and highlight promising research directions to bridge this gap. The design of invariant biometric representations remains a fundamental problem, despite recent attempts to address this issue through feature adaptation schemes. The difficulty in estimating the statistical distribution of biometric features not only hinders the development of better template protection algorithms but also diminishes the ability to quantify the noninvertibility and nonlinkability of existing algorithms. Finally, achieving nonlinkability without the use of external secrets (e.g., passwords) continues to be a challenging proposition. Further research on the above issues is required to cross the chasm between theory and practice in biometric ?template protection. 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. It is commonly accepted that users of a biometric system may have differing degrees of accuracy within the system. Some people may have trouble authenticating, while others may be particularly vulnerable to impersonation. Goats, wolves, and lambs are labels commonly applied to these problem users. These user types are defined in terms of verification performance when users are matched against themselves (goats) or when matched against others (lambs and wolves). The relationship between a user's genuine and impostor match results suggests four new user groups: worms, doves, chameleons, and phantoms. We establish formal definitions for these animals and a statistical test for their existence. A thorough investigation is conducted using a broad range of biometric modalities, including 2D and 3D faces, fingerprints, iris, speech, and keystroke dynamics. Patterns that emerge from the results expose novel, important, and encouraging insights into the nature of biometric match results. A new framework for the evaluation of biometric systems based on the biometric menagerie, as opposed to collective statistics, is proposed. Biometric recognition, or simply biometrics, is the science of establishing the identity of a person based on physical or behavioral attributes. It is a rapidly evolving field with applications ranging from securely accessing ones computer to gaining entry into a country. While the deployment of large-scale biometric systems in both commercial and government applications has increased the public awareness of this technology, "Introduction to Biometrics" is the first textbook to introduce the fundamentals of Biometrics to undergraduate graduate students. The three commonly used modalities in the biometrics field, namely, fingerprint, face, and iris are covered in detail in this book. Few other modalities like hand geometry, ear, and gait are also discussed briefly along with advanced topics such as multibiometric systems and security of biometric systems. Exercises for each chapter will be available on the book website to help students gain a better understanding of the topics and obtain practical experience in designing computer programs for biometric applications. These can be found at: http: www.csee.wvu.edu ross BiometricsTextBook .Designed for undergraduate and graduate students in computer science and electrical engineering, "Introduction to Biometrics" is also suitable for researchers and biometric and computer security professionals. In surveillance images, soft biometric attributes have been demonstrated to be quite effective in the problem of person re-identification. Many of these attributes can vary greatly, which has motivated a number of hand crafted features designed to recognize individual attributes. Although deep learning is generally useful in learning features appropriate for classification, it usually requires more data to train than what is available in most person re-identification databases. In this paper, we propose a residual network (MAResNet) deeper than current pedestrian attribute recognition, which we use to recognize multiple attributes simultaneously. The proposed network is both efficient and accurate. We recognize attributes at a rate of 271 FPS while simultaneously outperforming state of the art in attribute recognition on the PETA dataset. Biometrics are an important and widely used class of methods for identity verification and access control. Biometrics are attractive because they are inherent properties of an individual. They need not be remembered like passwords and are not easily lost or forged like identifying documents. At the same time, biometrics are fundamentally noisy and irreplaceable. There are always slight variations among the measurements of a given biometric, and, unlike passwords or identification numbers, biometrics are derived from physical characteristics that cannot easily be changed. The proliferation of biometric usage raises critical privacy and security concerns that, due to the noisy nature of biometrics, cannot be addressed using standard cryptographic methods. In this article, we present an overview of secure biometrics, also referred to as biometric template protection, an emerging class of methods that address these concerns. 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. Recently, pedestrian attributes like gender, age and clothing etc., have been used as soft biometric traits for recognizing people. Unlike existing methods that assume the independence of attributes during their prediction, we propose a multi-label convolutional neural network (MLCNN) to predict multiple attributes together in a unified framework. Firstly, a pedestrian image is roughly divided into multiple overlapping body parts, which are simultaneously integrated in the multi-label convolutional neural network. Secondly, these parts are filtered independently and aggregated in the cost layer. The cost function is a combination of multiple binary attribute classification cost functions. Moreover, we propose an attribute assisted person re-identification method, which fuses attribute distances and low-level feature distances between pairs of person images to improve person re-identification performance. Extensive experiments show: 1) the average attribute classification accuracy of the proposed method is 5.2 and 9.3 higher than the SVM-based method on three public databases, VIPeR and GRID, respectively; 2) the proposed attribute assisted person re-identification method is superior to existing approaches. State-of-the-art person re-identification methods seek robust person matching through combining various feature types. Often, these features are implicitly assigned with a single vector of global weights, which are assumed to be universally good for all individuals, independent to their different appearances. In this study, we show that certain features play more important role than others under different circumstances. Consequently, we propose a novel unsupervised approach for learning a bottom-up feature importance, so features extracted from different individuals are weighted adaptively driven by their unique and inherent appearance attributes. Extensive experiments on two public datasets demonstrate that attribute-sensitive feature importance facilitates more accurate person matching when it is fused together with global weights obtained using existing methods. Privacy is one of the most important social and political issues in our information society, characterized by a growing range of enabling and supporting technologies and services. Amongst these are communications, multimedia, biometrics, big data, cloud computing, data mining, internet, social networks, and audio-video surveillance. Each of these can potentially provide the means for privacy intrusion. De-identification is one of the main approaches to privacy protection in multimedia contents (text, still images, audio and video sequences and their combinations). It is a process for concealing or removing personal identifiers, or replacing them by surrogate personal identifiers in personal information in order to prevent the disclosure and use of data for purposes unrelated to the purpose for which the information was originally obtained. Based on the proposed taxonomy inspired by the Safe Harbour approach, the personal identifiers, i.e., the personal identifiable information, are classified as non-biometric, physiological and behavioural biometric, and soft biometric identifiers. In order to protect the privacy of an individual, all of the above identifiers will have to be de-identified in multimedia content. This paper presents a review of the concepts of privacy and the linkage among privacy, privacy protection, and the methods and technologies designed specifically for privacy protection in multimedia contents. The study provides an overview of de-identification approaches for non-biometric identifiers (text, hairstyle, dressing style, license plates), as well as for the physiological (face, fingerprint, iris, ear), behavioural (voice, gait, gesture) and soft-biometric (body silhouette, gender, age, race, tattoo) identifiers in multimedia documents. Privacy protection in multimedia.Taxonomy of the personal identifiers in multimedia contents.De-identification of non-biometrical identifiers.De-identification of physiological, behavioural and soft biometric identifiers. Recently, a kind of lightweight and resource-efficient biometrics-based security solutions were proposed for the emerging body sensor network (BSN). In such security solutions, physiological characteristics that can be captured by individual sensors of BSN were proposed to generate entity identifiers (EIs) for securing keying materials by a biometric approach. In this study, the authors focus on an improved key distribution solution with the energy distribution information of physiological signals (EDPSs) -based EIs. Firstly, different EDPS-based EI generation schemes are studied. Based on the existing multi-windows Fourier transform scheme, a modified one with single-window is proposed to improve the identification performance of the generated EIs. Then, a different method based on the discrete cosine transform of the autocorrelation sequence of physiological signals is proposed aiming for a significant increase in identification rates. The performances of time-varying randomness and identification rates are evaluated to examine EI's feasibility in securing the transmission of keying materials. Based on the characteristics of generated EIs, the corresponding key distribution solution, that is, user-dependent fuzzy vault, is proposed. A detailed system performance analysis in terms of half total error rate, anti-attack ability, as well as computational complexity, is conducted to demonstrate the effectiveness of the proposed solution. Facial expressions are a valuable source of information that accompanies facial biometrics. Early detection of physiological and psycho-emotional data from facial expressions is linked to the situational awareness module of any advanced biometric system for personal state re identification. In this article, a new method that utilizes both texture and geometric information of facial fiducial points is presented. We investigate Gauss–Laguerre wavelets, which have rich frequency extraction capabilities, to extract texture information of various facial expressions. Rotation invariance and the multiscale approach of these wavelets make the feature extraction robust. Moreover, geometric positions of fiducial points provide valuable information for upper lower face action units. The combination of these two types of features is used for facial expression classification. The performance of this system has been validated on three public databases: the JAFFE, the Cohn-Kanade, and the MMI image. Personal identity verification based on biometrics has received increasing attention since it allows reliable authentication through intrinsic characteristics, such as face, voice, iris, fingerprint, and gait. Particularly, face recognition techniques have been used in a number of applications, such as security surveillance, access control, crime solving, law enforcement, among others. To strengthen the results of verification, biometric systems must be robust against spoofing attempts with photographs or videos, which are two common ways of bypassing a face recognition system. In this paper, we describe an anti-spoofing solution based on a set of low-level feature descriptors capable of distinguishing between ‘live’ and ‘spoof’ images and videos. The proposed method explores both spatial and temporal information to learn distinctive characteristics between the two classes. Experiments conducted to validate our solution with datasets containing images and videos show results comparable to state-of-the-art approaches. Advances in the field of Information Technology also make Information Security an inseparable part of it. In order to deal with security, Authentication plays an important role. This paper presents a review on the biometric authentication techniques and some future possibilities in this field. In biometrics, a human being needs to be identified based on some characteristic physiological parameters. A wide variety of systems require 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 not anyone else. By using biometrics it is possible to confirm or establish an individual’s identity. The position of biometrics in the current field of Security has been depicted in this work. We have also outlined opinions about the usability of biometric authentication systems, comparison between different techniques and their advantages and disadvantages in this paper. Recent research in biometrics has suggested the existence of the “Biometric Menagerie” in which weak users contribute disproportionately to the error rate (FAR and FRR) of a biometric system. The aim of this work is to utilize this observation to design a multibiometric system where information is consolidated on a user-specific basis. To facilitate this, the users in a database are characterized into multiple categories and only users belonging to weak categories are required to provide additional biometric information. The contribution of this work lies in (a) the design of a selective fusion scheme where fusion is invoked only for a subset of users, and (b) evaluating the performance of such a scheme on two public datasets. Experiments on the multi-unit CASIA V3 iris database and multi-unit WVU fingerprint database indicate that selective fusion, as defined in this work, improves overall matching accuracy while potentially reducing overall computational time. This has positive implications in a large-scale system where the throughput can be substantially increased without compromising the verification accuracy of the system. We consider the task of predicting various traits of a person given an image of their face. We estimate both objective traits, such as gender, ethnicity and hair-color; as well as subjective traits, such as the emotion a person expresses or whether he is humorous or attractive. For sizeable experimentation, we contribute a new Face Attributes Dataset (FAD), having roughly 200,000 attribute labels for the above traits, for over 10,000 facial images. Due to the recent surge of research on Deep Convolutional Neural Networks (CNNs), we begin by using a CNN architecture for estimating facial attributes and show that they indeed provide an impressive baseline performance. To further improve performance, we propose a novel approach that incorporates facial landmark information for input images as an additional channel, helping the CNN learn better attribute-specific features so that the landmarks across various training images hold correspondence. We empirically analyse the performance of our method, showing consistent improvement over the baseline across traits. Biometric recognition offers a reliable solution to the problem of user authentication in identity management systems. With the widespread deployment of biometric systems in various applications, there are increasing concerns about the security and privacy of biometric technology. Public acceptance of biometrics technology will depend on the ability of system designers to demonstrate that these systems are robust, have low error rates, and are tamper proof. We present a high-level categorization of the various vulnerabilities of a biometric system and discuss countermeasures that have been proposed to address these vulnerabilities. In particular, we focus on biometric template security which is an important issue because, unlike passwords and tokens, compromised biometric templates cannot be revoked and reissued. Protecting the template is a challenging task due to intrauser variability in the acquired biometric traits. We present an overview of various biometric template protection schemes and discuss their advantages and limitations in terms of security, revocability, and impact on matching accuracy. A template protection scheme with provable security and acceptable recognition performance has thus far remained elusive. Development of such a scheme is crucial as biometric systems are beginning to proliferate into the core physical and information infrastructure of our society.