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Biometric template security

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TLDR
This work presents a high-level categorization of the various vulnerabilities of a biometric system and discusses countermeasures that have been proposed to address these vulnerabilities.
Abstract
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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Journal ArticleDOI

A survey on biometric cryptosystems and cancelable biometrics

TL;DR: A comprehensive survey of biometric cryptosystems and cancelable biometrics is presented and state-of-the-art approaches are reviewed based on which an in-depth discussion and an outlook to future prospects are given.
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Minutia Cylinder-Code: A New Representation and Matching Technique for Fingerprint Recognition

TL;DR: The Minutia Cylinder-Code is introduced, a novel representation based on 3D data structures (called cylinders), built from minutiae distances and angles and the feasibility of obtaining a very effective fingerprint recognition implementation for light architectures is demonstrated.
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50 years of biometric research

TL;DR: 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 societal impact.
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Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition

TL;DR: A novel software-based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts and the experimental results show that the proposed method is highly competitive compared with other state-of-the-art approaches.
Journal ArticleDOI

Biometric Antispoofing Methods: A Survey in Face Recognition

TL;DR: The goal of this paper is to provide a comprehensive overview on the work that has been carried out over the last decade in the emerging field of antispoofing, with special attention to the mature and largely deployed face modality.
References
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TL;DR: In this paper, the authors describe a general-purpose representation-independent method for the accurate and computationally efficient registration of 3D shapes including free-form curves and surfaces, based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point.
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Eigenfaces for recognition

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Eigenfaces vs. Fisherfaces: recognition using class specific linear projection

TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.

Feature selection based on mutual information: criteria ofmax-dependency, max-relevance, and min-redundancy

TL;DR: This work derives an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection, and presents a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers).