基于深度学习的污损指纹识别研究

吴震东; 王雅妮; 章坚武

doi:10.11999/JEIT161121

基于深度学习的污损指纹识别研究

doi: 10.11999/JEIT161121

1.
(杭州电子科技大学网络空间安全学院杭州 310018) ②(杭州电子科技大学通信工程学院杭州 310018)

基金项目:

国家重点研发计划(2016YFB0800201)，浙江省自然科学基金(LY16F020016)，浙江省重点科技创新团队项目(2013TD03)

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- 被引次数: 0
出版历程
- 收稿日期: 2016-10-21
- 修回日期: 2017-04-01
- 刊出日期: 2017-07-19

Fouling and Damaged Fingerprint Recognition Based on Deep Learning

1.
(School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China)
2.
(School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China)

Funds:

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)

摘要

摘要: 随着社会信息化水平的提高及不稳定因素的增加，人们迫切需要更加可靠的识别技术对身份进行认证。因此，利用生物特征进行鉴定已成为时下热潮。其中的指纹识别更是因其方便性和可靠性受到普遍认同。传统的指纹识别方法基于特征点比对寻求相似性，此种方法特征点寻找容易出错，且随着指纹的模糊、破坏、污损或是其他问题，均会使识别率明显降低。针对这些问题，该文提出基于深度卷积神经网络(CNN)的CBF-FFPF(Central Block Fingerprint and Fuzzy Feature Points Fingerprint)算法对污损指纹图像进行分类识别。CBF-FFPF算法提取指纹中心点分块图像及特征点模糊化图，合并后输入CNN网络，进行指纹深层特征识别。将该算法与基于主成分分析(KPCA)，超限学习机(ELM)和k近邻分类器(KNN)的指纹识别算法进行比较，实验结果表明，所提出的CBF-FFPF算法对污损指纹识别有更高的识别率和更好的鲁棒性。
- 指纹识别 /
- 卷积神经网络 /
- 分块指纹 /
- 指纹深层特征
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.
- Fingerprint identification /
- Convolution Neural Network (CNN) /
- Sub block fingerprint /
- Fingerprint deep character

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参考文献(18)

HAGHIGHAT M, ZONOUZ S, and ABDEL-MOTTALEB M. CloudID: Trustworthy cloud-based and cross-enterprise biometric identification[J]. Expert Systems with Applications, 2015, 42(21): 7905-7916. doi: 10.1016/j.eswa.2015.06.025.

JAIN A K, FENG J J, and NANDAKUMAR K. Fingerprint matching[J]. Computer, 2010, 43(2): 36-44. doi: 10.1109/MC. 2010.38.

LIU Eryun, ZHAO Heng, LIANG Jimin, et al. Random local region descriptor (RLRD): A new method for fixed-length feature representation of fingerprint image and its application to template protection[J]. Future Generation Computer Systems, 2012, 28(1): 236-243. doi: 10.1016/j.future.2011.01. 001.

PAULINO A A, FENG J J, and JAIN A K. Latent fingerprint matching using descriptor-based hough transform [J]. IEEE Transactions on Information Forensics and Security, 2011, 8(1): 31-45. doi: 10.1109/IJCB.2011.6117483.

FENG J J, ZHOU J, and JAIN A K. Orientation field estimation for latent fingerprint enhancement[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(4): 925-940.

KRISH R P, FIERREZ J, RAMOS D, et al. Pre-registration for improved latent fingerprint identification[C]. International Conference on Pattern Recognition, Stockholm, Sweden, 2014: 696-701. doi: 10.1109/ICPR.2014.130.

FANG B, WEN H, LIU R Z, et al. A new fingerprint thinning algorithm[C]. 2010 Chinese Conference on Pattern Recognition (CCPR), Changsha, China, 2010: 1-4. doi: 10.1109/CCPR.2010.5659273.

KRIZHEVSKY A, SUTSKEVER I, and HINTON G. ImageNet classification with deep convolutional neural networks[C]. Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada, USA, 2012: 1097-1105.

HINTON G and OSINDERO S. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554. doi: 10.1162/neco.2006.18.7.1527.

SAINATH T N, KINGSBURY B, SAON G, et al. Deep convolutional neural networks for large-scale speech tasks[J]. Neural Networks, 2015, 64(4): 39-48. doi: 10.1016/j.neunet. 2014.08.005.

LAVIN A. maxDNN: An efficient convolution kernel for deep learning with maxwell GPUs[OL]. https://arxiv.org/abs/ 1501.06633, 2015.

ARPIT D and NAMBOODIRI A. Fingerprint feature extraction from gray scale images by ridge tracing[C]. International Joint Conference on Biometrics, Washington DC, USA, 2011: 1-8. doi: 10.1109/IJCB.2011.6117533.

CHANG C C, HWANG S M, and BUEHRER D J. A shape recognition scheme based on relative distances of feature points from the centroid[J]. Pattern Recognition, 1991, 24(11): 1053-1063. doi: 10.1016/0031-3203(91)90121-K.

IWASOKUN G B and AKINYOKUN O C. Fingerprint singular point detection based on modified poincare index method[J]. International Journal of Signal Processing Image Processing Pattern Recognition, 2014, 7(5): 259-272. doi: 10.14257/ijsip.2014.7.5.23.

KUMAR R, HANMANDLU M, and CHANDRA P. An empirical evaluation of rotation invariance of LDP feature for fingerprint matching using neural networks[J]. International Journal of Computational Vision Robotics, 2014, 4(4): 330-348. doi: 10.1504/IJCVR.2014.065569.

WANG R, HAN C, WU Y, et al. Fingerprint classification based on depth neural network[OL]. https:// arxiv.org/abs/ 1409.5188,2014.

KANAAN L, MERHEB D, KALLAS M, et al. PCA and KPCA of ECG signals with binary SVM classification[C]. IEEE Workshop on Signal Processing Systems, Beirut, Lebanon, 2011: 344-348. doi: 10.1109/SiPS.2011.6089000.

HUANG G B, ZHU Q Y, and SIEW C K. Extreme learning machine: Theory and applications[J]. Neurocomputing, 2006, 70(1-3): 489-501. doi: 10.1016/j.neucom.2005.12.126.

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