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.
Highlights
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 [1,2,3,4,5,6,7] and biometric template protection schemes [8,9,10,11,12,13] require biometric features to be present in binary form before they can be implemented in practice
We extend the study of [25] to tackle the open problem of generating desirable binary strings that are simultaneously highly discriminative, informative, and privacy-protective by means of discretization based on linearly separable subcode (LSSC)
In this article, we have proposed a four-step approach to generate highly discriminative, informative, and privacyprotective binary representations based on a fixed-bitallocation principle
Summary
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 [1,2,3,4,5,6,7] and biometric template protection schemes [8,9,10,11,12,13] require biometric features to be present in binary form before they can be implemented in practice. It is utmost important to derive highly informative binary strings in coping with the rising encryption standard in the future
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