CBRC: a novel approach for cancelable biometric template generation using random permutation and Chinese Remainder Theorem

Cancelable Biometrics is a challenging research field in which a cancelable template corresponding to a biometric is generated without affecting the performance. These cancelable templates can be generated using various methods given in literature. However, the qualitative and quantitative performance of these methods is not satisfactory. To address this concern, here we propose a novel cancelable biometric template generation method based on Random Permutation and Chinese Remainder Theorem. In the proposed approach, cancelable biometric template is generated by employing secret image i.e. the original biometric and cover image(s) i.e. any other image(s). Random permutation provides visual protection against the original biometric but preserves the intensity values. Chinese Remainder Theorem is used as a transformation function on the output of random permutation step so as to ensure the intensity values are not revealed to any intruder. The proposed method generates Cancelable Biometric templates in the form of Secret Shares and is suitable for gray as well as color images. To demonstrate the efficacy of the proposed method, extensive experiments have been performed on eight different datasets viz. Carreira-Perpinan ear (CP) (gray and color), University of Tehran IRIS (UTIRIS) (color), Olivetti Research Laboratory face (ORL) (gray), Mathematical Analysis of Images ear (AMI) (color), Aleix Martinez and Robert Benavente face (AR) (color), and Indian Institute of Technology Delhi iris (IITD) (gray and color). Qualitative and quantitative analysis of the generated templates shows that the performance of the proposed method is best when considered simultaneously than several state-of-the-art methods. The main advantages of the proposed method include (i) no glimpses of the original biometric in the cancelable template, (ii) no storage of original biometric during enrolment (iii) the proposed method works for both gray as well as color images and (iv) no image registration is required.