Privacy-preserving biometrics using matrix random low-rank approximation approach

Abstract

In this paper, we propose a matrix random low-rank approximation (MRLRA) approach to generate cancelable biometric templates for privacy-preserving. MRLRA constructs a random low-rank matrix to approximate the hybridization of biometric feature and a random matrix. Theoretically analysis shows the distance between one cancelable low-rank biometric template by MRLRA and its original template is very small, which results to the verification and authentication performance by MRLRA is near that of original templates. Cancelable biometric templates by MRLRA conquer the weakness of random projection based cancelable biometric templates, in which the performance will deteriorate much under the same tokens. Experiments have verified that (i) cancelable biometric templates by MRLRA are sensitive to the user-specific tokens which are used for constructing the random matrix in MRLRA; (ii) MRLRA can reduce the noise of biometric templates; (iii)Even under the condition of same tokens, the performance of cancelable biometric templates by MRLRA doesn't deteriorate much.