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.

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Biometric template security

Two studies considered the interplay between user-perceived usability (i.e., pragmatic attributes), hedonic attributes (e.g., stimulation, identification), goodness (i.e., satisfaction), and beauty of 4 different MP3-player skins. As long as beauty and goodness stress the subjective valuation of a product, both were related to each other. However, the nature of goodness and beauty was found to differ. Goodness depended on both perceived usability and hedonic attributes. Especially after using the skins, perceived usability became a strong determinant of goodness. In contrast, beauty largely depended on identification; a hedonic attribute group, which captures the product's ability to communicate important personal values to relevant others. Perceived usability as well as goodness was affected by experience (i.e., actual usability, usability problems), whereas hedonic attributes and beauty remained stable over time. All in all, the nature of beauty is rather self-oriented than goal-oriented, whereas goodness relates to both.

The interplay of beauty, goodness, and usability in interactive products

https://www.cse.msu.edu/~rossarun/BiometricsTextBook/Papers/Iris/Bowyer_ImageUndIris_CVIU07.pdf

Image understanding for iris biometrics: A survey

Biometrics-based authentication systems offer obvious usability advantages over traditional password and token-based authentication schemes. However, biometrics raises several privacy concerns. A biometric is permanently associated with a user and cannot be changed. Hence, if a biometric identifier is compromised, it is lost forever and possibly for every application where the biometric is used. Moreover, if the same biometric is used in multiple applications, a user can potentially be tracked from one application to the next by cross-matching biometric databases. In this paper, we demonstrate several methods to generate multiple cancelable identifiers from fingerprint images to overcome these problems. In essence, a user can be given as many biometric identifiers as needed by issuing a new transformation "key". The identifiers can be cancelled and replaced when compromised. We empirically compare the performance of several algorithms such as Cartesian, polar, and surface folding transformations of the minutiae positions. It is demonstrated through multiple experiments that we can achieve revocability and prevent cross-matching of biometric databases. It is also shown that the transforms are noninvertible by demonstrating that it is computationally as hard to recover the original biometric identifier from a transformed version as by randomly guessing. Based on these empirical results and a theoretical analysis we conclude that feature-level cancelable biometric construction is practicable in large biometric deployments

Generating Cancelable Fingerprint Templates

The detection and recognition of objects in images is a key research topic in the computer vision community Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli: examines the basics of digital image formation, highlighting points critical to the task of template matching; presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets; discusses recent pattern classification paradigms from a template matching perspective; illustrates the development of a real face recognition system; explores the use of advanced computer graphics techniques in the development of computer vision algorithms Template Matching Techniques in Computer Vision is primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection It is also of interest to graduate students undertaking studies in these areas

Template Matching Techniques in Computer Vision: Theory and Practice

Biometric analysis for identity verification is becoming a widespread reality. Such implementations necessitate large-scale capture and storage of biometric data, which raises serious issues in terms of data privacy and (if such data is compromised) identity theft. These problems stem from the essential permanence of biometric data, which (unlike secret passwords or physical tokens) cannot be refreshed or reissued if compromised. Our previously presented biometric-hash framework prescribes the integration of external (password or token-derived) randomness with user-specific biometrics, resulting in bitstring outputs with security characteristics (i.e., noninvertibility) comparable to cryptographic ciphers or hashes. The resultant BioHashes are hence cancellable, i.e., straightforwardly revoked and reissued (via refreshed password or reissued token) if compromised. BioHashing furthermore enhances recognition effectiveness, which is explained in this paper as arising from the random multispace quantization (RMQ) of biometric and external random inputs

Random Multispace Quantization as an Analytic Mechanism for BioHashing of Biometric and Random Identity Inputs

We outline cryptographic key-computation from biometric data based on error-tolerant transformation of continuous-valued face eigenprojections to zero-error bitstrings suitable for cryptographic applicability. Bio-hashing is based on iterated inner-products between pseudorandom and user-specific eigenprojections, each of which extracts a single-bit from the face data. This discretisation is highly tolerant of data capture offsets, with same-user face data resulting in highly correlated bitstrings. The resultant user identification in terms of a small bitstring-set is then securely reduced to a single cryptographic key via Shamir secret-sharing. Generation of the pseudorandom eigenprojection sequence can be securely parameterised via incorporation of physical tokens. Tokenised bio-hashing is rigorously protective of the face data, with security comparable to cryptographic hashing of token and knowledge key-factors. Our methodology has several major advantages over conventional biometric analysis ie elimination of false accepts (FA) without unacceptable compromise in terms of more probable false rejects (FR), straightforward key-management, and cryptographically rigorous commitment of biometric data in conjunction with verification thereof.

David Chek Ling Ngo

Papers

Random Multispace Quantization as an Analytic Mechanism for BioHashing of Biometric and Random Identity Inputs

Computation of cryptographic keys from face biometrics

PalmHashing: a novel approach for cancelable biometrics

Modelling interface aesthetics

Personalised cryptographic key generation based on FaceHashing