Multihashing, human authentication featuring biometrics data and tokenized random number: A case study FVC2004

Given the recent explosion of interest in human authentication, verification based on tokenized pseudo-random numbers and the user-specific biometric feature has received much attention. These methods have significant functional advantages over solely biometrics, i.e. zero equal error rate. The main drawback of the methods proposed in the literature relies in exhibiting low performance when an “impostor” B steals the pseudo-random numbers of A and he tries to authenticate as A. In this paper, we show that a multimodal fusion, where only one biometric characteristic is combined with the pseudo-random numbers, permits to obtain a zero equal error rate when nobody steals the pseudo-random numbers, and good performance when an “impostor” B steals the pseudo-random numbers of A and he tries to authenticate as A. In this paper, we study the fusion among the score obtained by a Face Recognizer (where the face features are combined with pseudo-random numbers) and the scores of the systems submitted to FVC2004.