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.

提出了一种基于深度神经网络的通用生物识别身份验证新架构RegNet，将授权和未授权的用户映射到两个不同的高斯分布中，并通过学习映射而避免典型分类器中的边界复杂和难以分析的问题，实验证明该系统在安全度量方面具有很高的性能。

学习映射到正则化潜空间以进行生物识别认证