A discriminative parametric approach to video-based score-level fusion for biometric authentication

Video-based biometric systems are becoming feasible thanks to advancement in both algorithms and computation platforms. Such systems have many advantages: improved robustness to spoof attack, performance gain thanks to variance reduction, and increased data quality/resolution, among others. We investigate a discriminative video-based score-level fusion mechanism, which enables an existing biometric system to further harness the riches of temporarily sampled biometric data using a set of distribution descriptors. Our approach shows that higher order moments of the video scores contain discriminative information. To our best knowledge, this is the first time this higher order moment is reported to be effective in the score-level fusion literature. Experimental results based on face and speech unimodal systems, as well as multimodal fusion, show that our proposal can improve the performance over that of the standard fixed rule fusion strategies by as much as 50%.