  We propose a novel architecture for generic biometric authentication based on
deep neural networks: RegNet. Differently from other methods, RegNet learns a
mapping of the input biometric traits onto a target distribution in a
well-behaved space in which users can be separated by means of simple and
tunable boundaries. More specifically, authorized and unauthorized users are
mapped onto two different and well behaved Gaussian distributions. The novel
approach of learning the mapping instead of the boundaries further avoids the
problem encountered in typical classifiers for which the learnt boundaries may
be complex and difficult to analyze. RegNet achieves high performance in terms
of security metrics such as Equal Error Rate (EER), False Acceptance Rate (FAR)
and Genuine Acceptance Rate (GAR). The experiments we conducted on publicly
available datasets of face and fingerprint confirm the effectiveness of the
proposed system.
