Multiple decision templates with adaptive features for fingerprint classification

Jun Ki Min, Sung Bae Cho

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


This paper proposes a novel fingerprint classification method using multiple decision templates of Support Vector Machines (SVMs) with adaptive features. In order to overcome intra-class and inter-class ambiguities of fingerprints, the proposed method extracts a feature vector from an adaptively detected feature region and classifies the feature vector using SVMs. The outputs of the SVMs are then combined by multiple decision templates that make several per class. Experimental results on NIST4 fingerprint database revealed the effectiveness and validity of the proposed method for fingerprint classification.

Original languageEnglish
Pages (from-to)1323-1338
Number of pages16
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number8
Publication statusPublished - 2007 Dec

Bibliographical note

Funding Information:
This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the Biometrics Engineering Research Center (BERC) at Yonsei University.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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