An A-Contrario Biometric Fusion Approach

Luis Di Martino, Javier Preciozzi, Rafael Grompone von Gioi, Guillermo Garella, Alicia Fernandez, Federico Lecumberry; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 822-823

Fusion is a key component in many biometric systems: it is one of the most widely used techniques to improve their accuracy. Each time we need to combine the output of systems that use different biometric traits, or different samples of the same biometric trait, or even different algorithms, we need to define a fusion strategy. Independently of the fusion method used, there is always a decision step, in which it is decided if the traits being compared correspond to the same individual or not. In this work, we present a statistical decision criterion based on the a-contrario framework, which has already proven to be useful in biometric applications. The proposed method and its theoretical background is described in detail, and its application to biometric fusion is illustrated with simulated and real data.