Heterogeneous Information Fusion: A Novel Fusion Paradigm for Biometric Systems
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.