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)

Abstract

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
Pages1196-1200
Number of pages5
ISBN (Print)3540366679, 9783540366676
Publication statusPublished - 2006 Jan 1
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

Other

Other9th Pacific Rim International Conference on Artificial Intelligence
CountryChina
CityGuilin
Period06/8/706/8/11

Fingerprint

Fingerprint
Bayes Classifier
Classifiers
Tie
Ambiguous
Singularity
Calculate
Class

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Hong, J. H., & Cho, S. B. (2006). Dynamically subsumed-OVA SVMs for fingerprint classification. In PRICAI 2006: Trends in Artificial Intelligence - 9th Pacific Rim International Conference on Artificial Intelligence, Proceedings (pp. 1196-1200). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4099 LNAI). Springer Verlag.
Hong, Jin Hyuk ; Cho, Sung Bae. / Dynamically subsumed-OVA SVMs for fingerprint classification. PRICAI 2006: Trends in Artificial Intelligence - 9th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Springer Verlag, 2006. pp. 1196-1200 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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abstract = "A novel method to fingerprint classification, in which the na{\"i}ve 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.",
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year = "2006",
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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
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Hong, JH & Cho, SB 2006, Dynamically subsumed-OVA SVMs for fingerprint classification. in PRICAI 2006: Trends in Artificial Intelligence - 9th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4099 LNAI, Springer Verlag, pp. 1196-1200, 9th Pacific Rim International Conference on Artificial Intelligence, Guilin, China, 06/8/7.

Dynamically subsumed-OVA SVMs for fingerprint classification. / Hong, Jin Hyuk; Cho, Sung Bae.

PRICAI 2006: Trends in Artificial Intelligence - 9th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Springer Verlag, 2006. p. 1196-1200 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4099 LNAI).

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

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AU - Cho, Sung Bae

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N2 - 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.

AB - 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.

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M3 - Conference contribution

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SN - 9783540366676

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Hong JH, Cho SB. Dynamically subsumed-OVA SVMs for fingerprint classification. In PRICAI 2006: Trends in Artificial Intelligence - 9th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Springer Verlag. 2006. p. 1196-1200. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).