Random Permutation-Based Linear Discriminant Analysis for Cancelable Biometric Recognition

The increased use of biometrics in the present scenario has led to the concerns over security and privacy of the enrolled users. This is because the biometric traits like face, iris, ear, etc., are not cancelable or revocable. In case if the templates are compromised, the imposters may gain illegitimate access. To resolve such issues, a simple yet powerful technique called “random permutation-based linear discriminant analysis” for cancelable biometric recognition has been proposed in this paper. The proposed technique is established on the notion of a cancelable biometric system through which the biometric templates can be revoked and renewed. The proposed technique accepts the cancelable biometric template and a key (called PIN) issued to the user. The user’s identity is recognized only when both cancelable biometric template and PIN are valid, else the user is prohibited. The performance of the proposed technique is demonstrated on the freely available face (ORL), iris (UBIRIS), and ear (IITD) datasets against state-of-the-art methods. The key benefits of the proposed technique are (i) classification accuracy remains unaffected by using random permutation and (ii) robustness across different biometric traits.