Fingerprint classification using one-vs-all support vector machines dynamically ordered with nai{dotless}̈ve Bayes classifiers

Jin Hyuk Hong, Jun Ki Min, Ung Keun Cho, Sung Bae Cho

Research output: Contribution to journalArticle

91 Citations (Scopus)

Abstract

Fingerprint classification reduces the number of possible matches in automated fingerprint identification systems by categorizing fingerprints into predefined classes. Support vector machines (SVMs) are widely used in pattern classification and have produced high accuracy when performing fingerprint classification. In order to effectively apply SVMs to multi-class fingerprint classification systems, we propose a novel method in which the SVMs are generated with the one-vs-all (OVA) scheme and dynamically ordered with nai{dotless}̈ve Bayes classifiers. This is necessary to break the ties that frequently occur when working with multi-class classification systems that use OVA SVMs. More specifically, it uses representative fingerprint features as the FingerCode, singularities and pseudo ridges to train the OVA SVMs and nai{dotless}̈ve Bayes classifiers. The proposed method has been validated on the NIST-4 database and produced a classification accuracy of 90.8% for five-class classification with the statistical significance. The results show the benefits of integrating different fingerprint features as well as the usefulness of the proposed method in multi-class fingerprint classification.

Original languageEnglish
Pages (from-to)662-671
Number of pages10
JournalPattern Recognition
Volume41
Issue number2
DOIs
Publication statusPublished - 2008 Feb 1

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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