Fingerprint classification using one-vs-all support vector machines dynamically ordered with naive Bayes classifiers

@article{Hong2008FingerprintCU,
  title={Fingerprint classification using one-vs-all support vector machines dynamically ordered with naive Bayes classifiers},
  author={Jin-Hyuk Hong and Jun-Ki Min and Ung-Keun Cho and Sung-Bae Cho},
  journal={Pattern Recognition},
  year={2008},
  volume={41},
  pages={662-671}
}
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… CONTINUE READING
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  • 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.

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