Random permutation principal component analysis for cancelable biometric recognition

Although biometrics is being increasingly used across the world, it also raises concerns over privacy and security of the enrolled identities. This is due to the fact that biometrics are not cancelable and if compromised may give access to the intruder. To address these problems, in this paper, we suggest two simple and powerful techniques called (i) Random Permutation Principal Component Analysis (RP-PCA) and (ii) Random Permutation Two Dimensional Principal Component Analysis (RP-2DPCA). The proposed techniques are based on the idea of cancelable biometric which can be reissued if compromised. The proposed techniques work in a cryptic manner by accepting the cancelable biometric template and a key (called PIN) issued to a user. The identity of a person is recognized only if the combination of template and PIN is valid, otherwise the identity is rejected. The superiority 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 key advantages of the proposed techniques are (i) classification accuracy remains unaffected due to cancelable biometric templates generated using random permutation (ii) robustness across different biometrics. In addition, no image registration is required for performing recognition.