Effective Fingerprint Classification by Localized Models of Support Vector Machines

  • Jun-Ki Min
  • Jin-Hyuk Hong
  • Sung-Bae Cho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

Fingerprint classification is useful as a preliminary step of the matching process and is performed in order to reduce searching time. Various classifiers like support vector machines (SVMs) have been used to fingerprint classification. Since the SVM which achieves high accuracy in pattern classification is a binary classifier, we propose a classifier-fusion method, multiple decision templates (MuDTs). The proposed method extracts several clusters of different characteristics from each class of fingerprints and constructs localized classification models in order to overcome restrictions to ambiguous fingerprints. Experimental results show the feasibility and validity of the proposed method.

Keywords

Support Vector Machine Fingerprint Image NIST Database Classifier Fusion Propose Method Extract 
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 2005

Authors and Affiliations

  • Jun-Ki Min
    • 1
  • Jin-Hyuk Hong
    • 1
  • Sung-Bae Cho
    • 1
  1. 1.Department of Computer Science, Biometrics Engineering Research CenterYonsei UniversitySeoulKorea

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