An effective biometric discretization approach to extract highly discriminative, informative, and privacy-protective bin
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RESEARCH
Open Access
An effective biometric discretization approach to extract highly discriminative, informative, and privacy-protective binary representation Meng-Hui Lim and Andrew Beng Jin Teoh*
Abstract Biometric discretization derives a binary string for each user based on an ordered set of biometric features. This representative string ought to be discriminative, informative, and privacy protective when it is employed as a cryptographic key in various security applications upon error correction. However, it is commonly believed that satisfying the first and the second criteria simultaneously is not feasible, and a tradeoff between them is always definite. In this article, we propose an effective fixed bit allocation-based discretization approach which involves discriminative feature extraction, discriminative feature selection, unsupervised quantization (quantization that does not utilize class information), and linearly separable subcode (LSSC)-based encoding to fulfill all the ideal properties of a binary representation extracted for cryptographic applications. In addition, we examine a number of discriminative feature-selection measures for discretization and identify the proper way of setting an important feature-selection parameter. Encouraging experimental results vindicate the feasibility of our approach. Keywords: biometric discretization, quantization, feature selection, linearly separable subcode encoding
1. Introduction Binary representation of biometrics has been receiving an increased amount of attention and demand in the last decade, ever since biometric security schemes were widely proposed. Security applications such as biometric-based cryptographic key generation schemes [1-7] and biometric template protection schemes [8-13] require biometric features to be present in binary form before they can be implemented in practice. However, as security is in concern, these applications require binary biometric representation to be • Discriminative: Binary representation of each user ought to be highly representative and distinctive so that it can be derived as reliably as possible upon every query request of a genuine user and will neither be misrecognized as others nor extractable by any non-genuine user. • Informative: Information or uncertainty contained in the binary representation of each user should be made adequately high. In fact, the use of a huge number of * Correspondence: [email protected] School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, South Korea
equal-probable binary outputs creates a huge key space which could render an attacker clueless in guessing the correct output during a brute force attack. This is extremely essential in security provision as a malicious impersonation could take place in a straightforward manner if the correct key can be obtained by the adversary with an overwhelming probability. Entropy is a common measure of uncertainty, and it is usually a biometric system specification. By denoting the entropy of a bi
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