A face template protection scheme based on chaotic map, error correction code and locality sensitive hashing

Abstract Face recognition has been widely used in many fields and has become an important identification method. The templates in face recognition systems are associated with the facial biometric features of users, and once leaked, it will pose a persistent threat to the users. Therefore, it is particularly important to protect the security of templates. In this work, a novel face template protection scheme is proposed by combining chaotic map, error correction code and locality sensitive hashing. The scheme utilizes two sets of parameters: global keys and user keys, and the generated data consists of two parts: storage key and biometric template. When generating a template, the extracted feature vector is permuted by using chaotic sequences to disrupt the correlation between different dimensions. Then, the user keys are processed by error correction code to generate the storage key, which can be used to recover the user keys during authentication. Finally, the permuted vector is processed by the proposed random number based locality sensitive hashing to generate biometric template. Experimental results and theoretical analysis show that the scheme has good accuracy and security, and can effectively resist various attacks on the face template.