Learning mappings onto regularized latent spaces for biometric authentication
This addresses biometric authentication for security applications, but it is incremental as it builds on existing deep learning methods with a novel mapping approach.
The paper tackles biometric authentication by proposing RegNet, a deep neural network architecture that maps biometric traits onto regularized Gaussian distributions for authorized and unauthorized users, achieving high performance on security metrics like EER, FAR, and GAR in experiments on face and fingerprint datasets.
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.