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Learning Features for Fingerprint Classification

  • Xuejun Tan
  • Bir Bhanu
  • Yingqiang Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2688)

Abstract

In this paper, we present a fingerprint classification approach based on a novel feature-learning algorithm. Unlike current research for fingerprint classification that generally uses visually meaningful features, our approach is based on Genetic Programming (GP), which learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. Our experimental results show that our approach can find good composite operators to effectively extract useful features. Using a Bayesian classifier, without rejecting any fingerprints from NIST-4, the correct rates for 4 and 5-class classification are 93.2% and 91.2% respectively, which compare favorably and have advantages over the best results published to date.

Keywords

Feature Vector Genetic Programming Learn Feature Composite Operator Primitive Feature 
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 2003

Authors and Affiliations

  • Xuejun Tan
    • 1
  • Bir Bhanu
    • 1
  • Yingqiang Lin
    • 1
  1. 1.Center for Research in Intelligent SystemsUniversity of CaliforniaRiversideUSA

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