Protecting DCT Templates for a Face Verification System by Means of Pseudo-random Permutations

摘要

Biometric template security and privacy are a great concern of bio-metric systems, because unlike passwords and tokens, compromised biometric templates cannot be revoked and reissued. In this paper we present a protection scheme for an identity verification system through biometrical face recognition based on a user dependent pseudo-random ordering of the DCT template coefficients and MPL and RBF Neural Networks for classification. In addition to privacy enhancement, because a hacker can hardly match a fake biometric sample without knowing the pseudo-random ordering this scheme, it also increases the biometric recognition performance.