An Integrated Dual Factor Authenticator Based on the Face Data and Tokenised Random Number

Abstract

This paper proposed a novel integrated dual factor authenticator based on iterated inner products between tokenised pseudo random number and the user specific facial feature, which generated from a well known subspace feature extraction technique- Fisher Discriminant Analysis, and hence produce a set of user specific compact code that coined as BioCode. The BioCode highly tolerant of data captures offsets, with same user facial data resulting in highly correlated bitstrings. Moreover, there is no deterministic way to get the user specific code without having both tokenised random data and user facial feature. This would protect us for instance against biometric fabrication by changing the user specific credential, is as simple as changing the token containing the random data. This approach has significant functional advantages over solely biometrics ie. zero EER point and clean separation of the genuine and imposter populations, thereby allowing elimination of FARs without suffering from increased occurrence of FRRs.