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.
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.
[2]
JAIN A K, FENG J J, and NANDAKUMAR K. Fingerprint matching[J]. Computer, 2010, 43(2): 36-44. doi: 10.1109/MC. 2010.38.
[3]
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.
[4]
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.
[5]
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.
[6]
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.
[7]
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.
[8]
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.
[9]
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.
[10]
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.
[11]
LAVIN A. maxDNN: An efficient convolution kernel for deep learning with maxwell GPUs[OL]. https://arxiv.org/abs/ 1501.06633, 2015.
[12]
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.
[13]
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.
[14]
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.
[15]
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.
[16]
WANG R, HAN C, WU Y, et al. Fingerprint classification based on depth neural network[OL]. https:// arxiv.org/abs/ 1409.5188,2014.
[17]
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.
[18]
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.
