Cancelable palmprint templates based on random measurement and noise data for security and privacy-preserving authentication

Abstract In order to achieve biometric security and enhance privacy-preserving, a novel palmprint template protection scheme based on random comparison and noise data is proposed. Firstly, Anisotropic Filter (AF) is employed to capture the orientation information of the palmprint. Then, the orientation feature of palmprint is measured by a chaotic matrix to generate secure and cancelable palmprint template. The pseudo-randomness and non-ergodicity of the chaotic matrix can guarantee the security of template. Finally, in order to enhance the privacy protection of the template, the noise data with independent and identically distributed is added, as the final cancelable palmprint template. Theoretical analysis shows that a proper amount of noise has little effect on the recognition accuracy while the privacy is enhanced. During the matching stage, the recognition accuracy can be improved by fusing matching scores at the score-layer or the decision-layer. The theoretical effect of adding noise on the performance is also analyzed. The matching scores of the experimental results are consistent with the theoretical values, which means that we can reasonably adjusted the proportion of noise data through calculations and protect palmprint privacy on the basis of ensuring the recognition accuracy. Furthermore, our methods can still achieve very high security in the worst case of secret key theft.