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Dempster-Shafer Theory Based Classifier Fusion for Improved Fingerprint Verification Performance

  • Richa Singh
  • Mayank Vatsa
  • Afzel Noore
  • Sanjay K. Singh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)

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.

Keywords

Fusion Algorithm Belief Function Focal Element Conditioning Rule Fusion Combination 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Richa Singh
    • 1
  • Mayank Vatsa
    • 1
  • Afzel Noore
    • 1
  • Sanjay K. Singh
    • 2
  1. 1.West Virginia UniversityMorgantownUSA
  2. 2.Institute of Engineering and TechnologyJaunpurIndia

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