A supervised method to discriminate between impostors and genuine in biometry

In this paper we describe a supervised technique that allows to develop a more robust biometric system with respect to those based directly on the similarities of the biometric matchers or on the similarities normalized by the unconstrained cohort normalisation. In order to discriminate between genuine and impostors a Quadratic Discriminant classifier is trained using four features: the similarities of the biometric matcher; the similarities of the biometric matcher after the Unconstrained Cohort Normalisation (UCN); the average scores among the test pattern and the users that belong to the Background model; the difference between the user-specific threshold and the user-independent threshold. The proposed technique is validated by extensive experiments carried out on several biometric datasets (Palm, Finger, 2D and 3D Faces, Ear). The experimental results demonstrate that the capabilities provided by our supervised method can significantly improve the performance of a standard biometric matcher or the performance of the standard UCN.