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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 paper 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.
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