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={J. Hong and Jun-Ki Min and U. Cho and S. Cho},
  journal={Pattern Recognit.},
  year={2008},
  volume={41},
  pages={662-671}
}
  • J. Hong, Jun-Ki Min, +1 author S. Cho
  • Published 2008
  • Computer Science
  • Pattern Recognit.
  • Fingerprint classification reduces the number of possible matches in automated fingerprint identification systems by categorizing fingerprints into predefined classes. [...] Key Method More specifically, it uses representative fingerprint features as the FingerCode, singularities and pseudo ridges to train the OVA SVMs and nai@?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…Expand Abstract
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