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2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems
This paper formulates an evidence theoretic multimodal fusion approach using belief functions that takes into account the variability in image characteristics. When processing non-ideal images the variation in the quality of features at different levels of abstraction may cause individual classifiers to generate conflicting genuine-impostor decisions. Existing fusion approaches are non-adaptive and do not always guarantee optimum performance improvements. We propose a contextual unificationdoi:10.1109/btas.2007.4401963 fatcat:x6uuzisy6nayddrqcfwqngke6q