An effective biometric discretization approach to extract highly discriminative, informative, and privacy-protective binary representationReport as inadecuate




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EURASIP Journal on Advances in Signal Processing

, 2011:107

First Online: 19 November 2011Received: 11 March 2011Accepted: 19 November 2011

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.

Keywordsbiometric discretization quantization feature selection linearly separable subcode encoding Electronic supplementary materialThe online version of this article doi:10.1186-1687-6180-2011-107 contains supplementary material, which is available to authorized users.

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Author: Meng-Hui Lim - Andrew Beng Jin Teoh

Source: https://link.springer.com/



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