Fingerprint pattern classification algorithm based on deep convolutional neural networks

doi:10.7523/j.issn.2095-6134.2016.06.013

Abstract

Abstract:

The accuracies of traditional fingerprint pattern classification algorithms rely heavily on the corresponding feature extraction algorithms. Further more, the within-class variance of fingerprint patterns increases while the between-class variance decreases in large-scale database. So it is difficult for hand-designed features to suit with all fingerprint data. In order to remove the coupling with hand-designed feature extraction algorithms, we propose an approach to directly recognize patterns in raw fingerprint images. It takes advantage of the automatic feature extraction ability of convolutional neural networks to learn patterns from large amount of images. The training data are carefully designed to fit the variety of fingerprints and to improve the robutness. Meanwhile, the accuracy is further improved by averaging multi-scale models. In our experiment, an accuracy of 94.2% for four-class classification has been achieved in the international opening fingerprint dataset NIST DB 4. Our algorithm surpasses many classical algorithms, and it is both practical and meaningful.

Key words: fingerprint pattern classification, convolutional neural networks, fingerprint recognition

CLC Number:

TP391

JIANG Lu, ZHAO Tong, WU Min. Fingerprint pattern classification algorithm based on deep convolutional neural networks[J]. , 2016, 33(6): 808-814.

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