Personal Authenticator on the Basis of Two-Factors: Palmprint Features and Tokenized Random Data

Abstract

This paper presents a novel two-factor authenticator which hashes tokenized random data and moment based palmprint features to produce a set of private binary string, coined as Discrete-Hashing code. This novel technique requires two factors (random number + authorized biometrics) credentials in order to access the authentication system. Absence of either factor will just handicap the progress of authentication. Besides that, Discrete-Hashing also possesses high discriminatory power, with highly correlated bit strings for intra-class data. Experimental results show that this two-factor authenticator surpasses the classic biometric authenticator in terms of verification rate. Our proposed approach provides a clear separation between genuine and imposter population distributions. This implies that Discrete-Hashing technique allows achievement of zero False Accept Rate (FAR) without jeopardizing the False Reject Rate (FRR) performance, which is hardly possible to conventional biometric systems.