Dempster-Shafer theory based classifier fusion for improved fingerprint verification performance

Authors:
Richa Singh;Mayank Vatsa;Afzel Noore;Sanjay K. Singh
Affiliations:
West Virginia University, Morgantown, WV;West Virginia University, Morgantown, WV;West Virginia University, Morgantown, WV;Institute of Engineering and Technology, Jaunpur, UP, India
Venue:
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Year:
2006

Citing 4
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IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference

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Abstract

This paper presents a Dempster Shafer theory based classifier fusion algorithm to improve the performance of fingerprint verification. The proposed fusion algorithm combines decision induced match scores of minutiae, ridge, fingercode and pore based fingerprint verification algorithms and provides an improvement of at least 8.1% in the verification accuracy compared to the individual algorithms. Further, proposed fusion algorithm outperforms by at least 2.52% when compared with existing fusion algorithms. We also found that the use of Dempster's rule of conditioning reduces the training time by approximately 191 seconds.