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| Fouling and Damaged Fingerprint Recognition Based on Deep Learning |
| WU Zhendong① WANG Yani② ZHANG Jianwu② |
①(School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China)
②(School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China) |
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Abstract With the development of information technology and the increasing demanding of information security, people are urgently in need of more reliable identification techniques for identity authentication. Therefore, the biometric recognition methods have become a compelling issue. Among the methods, the fingerprint identification technique attracts much interest due to its excellent feasibility and reliability performance. The traditional fingerprint recognition method is based on matching feature points. However, this method needs a long time to find the feature points, and suffering the blur, scaling, damage, and other problems, the recognition rate is decreased seriously. To solve these problems, a fouling and damaged fingerprint recognition algorithm named CBF-FFPF (Central Block Fingerprint and Fuzzy Feature Points Fingerprint) is proposed, it is based on Convolution Neural Network (CNN) of deep learning. Combining small sub block fingerprint, which takes the fingerprint core point as the center from the thinned image and fuzzy graph of fingerprint feature points, as original image input to obtain the recognition rate. The recognition rate based on CBF-FFPF is compared with the fingerprint identification algorithm based on Kernel Principal Component Analysis (KPCA), Extreme Learning Machine (ELM), and K-Nearest Neighbor (KNN). Experimental results show that fingerprint recognition algorithm CBF- FFPF has higher recognition rate and better robustness.
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Received: 21 October 2016
Published: 11 May 2017
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| Fund: The National Key Research and Development Program of China (2016YFB0800201), The Natural Science Fundation of Zhejiang Province (LY16F020016), Zhejiang Provincial Science and Technology Innovation Program (2013TD03) |
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Corresponding Authors:
WU Zhendong
E-mail: wzd@hdu.edu.cn
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