Cancelable Binary Face Templates Generation Based on Partial Cake-Cutting Transformation and Spherical Hashing

With the rapid development of Internet of Things (IoT), biometric-based authentication systems have been widely used for access control. The wide application of biometric recognition systems has brought convenience but also raised privacy and security concerns. When unprotected templates are stolen, it will permanently leak the original biometric data. Therefore, it is important to ensure the security of biometric templates while meeting the real-time device requirements. Motivated by these issues, in this paper, we proposed a scheme based on partial Cake-cutting transformation and spherical hashing to generate cancelable binary face templates. Firstly, with external random parameters, partial Cake-cutting transformation is established to introduce randomness and preserve the relative distance similarity of face features. Then spherical hashing is utilized to encode the face features into protected binary codes. The protected template has the advantages of high entropy value, low storage consumption, and fast generation speed. Extensive experiments conducted on LFW, CFPW, and CASIA-FaceV5 databases along with theoretical analyses indicate that the proposed scheme shows good matching accuracy and strong resistance to various attacks. Besides, the protected templates can achieve equal or even better accuracy than the unprotected counterparts. Furthermore, the proposed scheme also satisfies the requirements of cancelable biometrics, i.e., irreversibility, revocability, and unlinkability.