Face matching with an a contrario false detection control

All biometric systems have two major functions: the identification of a given template on a biometric database and the verification that two templates correspond to the same subject. Although in both operations the response confidence of the system is of great importance, in the verification process it becomes crucial. Indeed we want to determine, with a very low error, whether two templates correspond to the same subject or not. Most of the work devoted to biometrics are related to other stages of the process: the preprocessing, feature extraction or even the definition of robust metrics to compare them. Nevertheless, few works exist on the criteria used to the acceptance of a matching between two templates. In this work we focus on this decision criterion: we introduce a novel strategy based on the a contrario framework. We show several advantages of using this framework in the context of biometrics: it is automatically adapted to the data, it allows us to control the performance of the system in advance and can be used directly in the matching stage not requiring a prior training stage. In order to show the framework on a practical situation, we implement a face recognition system. We perform several experiments to validate this novel strategy using different databases, both private and public. Also the robustness of this technique is evaluated using different features and metrics. The results show that the proposed approach outperforms classic methods, with a consistent theory behind it, that can be naturally adapted to any biometric system.