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Fingerprint classification using one-vs-all support vector machines dynamically ordered with naive Bayes classifiers
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DOI：10.1016/j.patcog.2007.07.004.png)
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Abstract
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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 naive 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 naive 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. (c) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keywords:
fingerprint classification
support vector machine
FingerCode
naive bayes classifier
singularity
pseudo ridges
dynamic classification
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