An increasing body of evidence suggests that cohort-based score normalization can improve the performance of biometric authentication. This approach relies on the use of N cohort biometric templates, which can be computationally expensive. We contribute to the advancement of cohort score normalization in two ways. First, we show both theoretically and empirically that the most similar and the most dissimilar cohort templates to a target user contain discriminative information. We then investigate the extraction of this information using polynomial regression. Extensive evaluation on the face and fingerprint modalities in the Biosecure DS2 dataset indicates that the proposed method outperforms the state-of-the-art cohort score normalization methods, while reducing the computation cost by as much as half.

One of the most promising ways to improve biometric person recognition is indisputably via information fusion, that is, to combine different sources of information. This pa per proposes a novel fusion paradigm that combines heterogeneous sources of information such as user-specific, cohort and quality information. Two formulations of this problem are proposed, differing in the assumption on the independence of the information sources. Unlike the more common multimodal/multi-algorithmic fusion, the novel paradigm has to deal with information that is not necessarily discriminative but still it is relevant. The methodology can be applied to any biometric system. Furthermore, extensive experiments based on 30 face and fingerprint experiments indicate that the performance gain with respect to the baseline system is about 30%. In contrast, solving this problem using conventional fusion paradigm leads to degraded results.