Unification of Evidence Theoretic Fusion Algorithms: A Case Study in Level-2 and Level-3 Fingerprint Features

Mayank Vatsa, Richa Singh, Afzel Noore
2007 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 unification
more » ... ework to dynamically select the most appropriate evidence theoretic fusion algorithm for a given scenario. The effectiveness of our approach is experimentally validated by fusing match scores from level-2 and level-3 fingerprint features. Compared to existing fusion algorithms, the proposed approach is computationally efficient, and the verification accuracy is not compromised even when conflicting decisions are encountered.
doi:10.1109/btas.2007.4401963 fatcat:x6uuzisy6nayddrqcfwqngke6q