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This paper formulates an evidence-theoretic multimodal unification approach using belief functions that take into account the variability in biometric image characteristics. While processing nonideal 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...
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