Dynamically subsumed-OVA SVMs for fingerprint classification

Jin Hyuk Hong, Sung Bae Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


A novel method to fingerprint classification, in which the naïve Bayes classifier (NB) and OVA SVMs are integrated, is presented. In order to solve the tie problem of combing OVA SVMs, we propose a subsumption architecture dynamically organized by the probability of classes. NB calculates the probability using singularities and pseudo codes, while OVA SVMs are trained on FingerCode. The proposed method not only tolerates ambiguous fingerprint images by combining different fingerprint features, but produces a classification accuracy of 90.8% for 5-class classification on the NIST 4 database, that is higher than conventional methods.

Original languageEnglish
Title of host publicationPRICAI 2006
Subtitle of host publicationTrends in Artificial Intelligence - 9th Pacific Rim International Conference on Artificial Intelligence, Proceedings
PublisherSpringer Verlag
Number of pages5
ISBN (Print)3540366679, 9783540366676
Publication statusPublished - 2006
Event9th Pacific Rim International Conference on Artificial Intelligence - Guilin, China
Duration: 2006 Aug 72006 Aug 11

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4099 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other9th Pacific Rim International Conference on Artificial Intelligence

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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