RP-LPP : a random permutation based locality preserving projection for cancelable biometric recognition

Abstract

Biometrics are being increasingly used across the world, but it also raises privacy and security concerns of the enrolled identities. The main reason is due to the fact that biometrics are not cancelable and if compromised may give access to the intruder. Cancelable biometric template is a solution to this problem which can be reissued if compromised. In this paper, we suggest a simple and powerful method called Random Permutation Locality Preserving Projection (RP-LPP) for Cancelable Biometric Recognition. Here, we exploit the mathematical relationship between the eigenvalues and eigenvectors of the original biometric image and its randomly permuted version is exploited for carrying out cancelable biometric recognition. The proposed technique work in a cryptic manner by accepting the cancelable biometric template and a key (called PIN) issued to a user. The effectiveness of the proposed techniques is demonstrated on three freely available face (ORL), iris (UBIRIS) and ear (IITD) datasets against state-of-the-art methods. The advantages of proposed technique are (i) the classification accuracy remains unaffected due to cancelable biometric templates generated using random permutation, (ii) security and quality of generated templates and (iii) robustness across different biometrics. In addition, no image registration is required for performing recognition.