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

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 indivi...

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Bibliographic Details
Published in:IEEE transactions on systems, man and cybernetics. Part A, Systems and humans man and cybernetics. Part A, Systems and humans, 2009-01, Vol.39 (1), p.47-56
Main Authors: Vatsa, M., Singh, R., Noore, A.
Format: Article
Language:English
Subjects:
Algorithms
Biometric fusion
Biometrics
Computational efficiency
Computational intelligence
Computer science
Cybernetics
Decisions
evidence-theoretic fusion
Fingerprint recognition
fingerprint verification
Fingerprints
Fusion power generation
Image quality
Learning systems
Mathematical analysis
Optimization
Support vector machine classification
Support vector machines
unification
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Summary: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 nonadaptive and do not always guarantee optimum performance improvements. We propose a contextual unification framework to dynamically select the most appropriate evidence-theoretic fusion algorithm for a given scenario. In the first approach, the unification framework uses deterministic rules to select the most appropriate fusion algorithm; while in the second approach, the framework intelligently learns from the input evidences using a 2nu-granular support vector machine. The effectiveness of the unification approach is experimentally validated by fusing match scores from level-2 and level-3 fingerprint features. Compared to existing fusion algorithms, the proposed unification approach is computationally efficient, and the verification accuracy is not compromised even when conflicting decisions are encountered.
ISSN:1083-4427
2168-2216
1558-2426
2168-2232
DOI:10.1109/TSMCA.2008.2007981