Abstract The security of biometric data in biometric‐based authentication systems is a significant concern. Cancellable biometrics aim to generate templates that can be replaced by new templates if compromised. We propose a new approach for generating cancellable biometric templates based on linear regression with random permutation. Our approach generates a virtual image for every biometric image by applying linear regression. In the next step, the cancellable biometric template is produced by randomly permuting each virtual image depending on a key assigned to each individual. If the template is compromised, it can be cancelled, and a new template can be generated by altering the key. Our method has shown superior performance compared to existing random permutation‐based methods in terms of authentication accuracy across six databases, encompassing face, iris, and ear, even when dealing with low‐resolution images. It performed well on challenging databases like UBIRIS and Georgia Tech, demonstrating the robustness of the proposed approach.

Single sample face recognition (SSFR) is a challenging research problem in which only one face image per person is available for training. Moreover, the face image may have different pose, expression, illumination, occlusion etc. rendering this problem more complex. Several methods have been suggested by various researchers in literature to solve SSFR. Here, we provide a comprehensive review of the methods proposed in the last decade for solving SSFR problem and introduce a novel taxonomy for the same. We divide SSFR methods broadly into five categories viz. (i) feature based, (ii) virtual sample generation based, (iii) generic database based, (iv) Hybrid and (v) other methods. We have also briefly reviewed the face databases used for evaluating single sample face recognition methods. Furthermore, the performance of the methods has been analyzed in terms of classification accuracy as given in literature. At last, we also suggest some future direction to the researchers and practitioners working in this fascinating research area.