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Fingerprint Classification Method based on J-divergence Entropy and SVM |
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PP: 245-251 |
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Author(s) |
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Gongping Yang,
Yilong Yin,
Xiuyan Qi,
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Abstract |
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| Fingerprint classification is one of the key technologies in Automatic Fingerprint Identification System (AFIS). However,
the performance of most recent fingerprint classification methods is low when the quality of the fingerprint image was low. To overcome
this problem, a novel method based on j-divergence entropy and SVM (Support Vector Machine) classifier is proposed in this paper.
Firstly, our method transforms the fingerprint images from spatial domain to frequency domain and constructs the directional images
according to frequency spectrum energy. Secondly, eigenvector around the core point is extracted. Thirdly, the dimension of eigenvector
is reduced by j-divergence entropy. At last, the input image is classified by SVM classifier. Experimental results on NIST-4 database
show the validity of our method, and the classification accuracy reaches 94.7% for four-class classification and 91.5% for five-class
classification with zero rejection rate. |
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