Finger-knuckle-print recognition using Gabor feature and MMDA

doi:10.1007/s11460-011-0141-3

Front Elect Electr Eng Chin

0, Vol.

Issue () : 374-380 https://doi.org/10.1007/s11460-011-0141-3

RESEARCH ARTICLE

Finger-knuckle-print recognition using Gabor feature and MMDA

Wankou YANG(

), Changyin SUN, Zhenyu WANG

School of Automation, Southeast University, Nanjing 210096, China

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Abstract

Recently, a new biometrics recognition, finger-knuckle-print, has attracted the interest of researchers. The popular techniques used in face recognition are not applied in finger-knuckle-print recognition. Inspired by the success of Gabor in face recognition, we propose a method that uses Gabor feature and a multi-manifold discriminant analysis (MMDA) method to identify finger-knuckle-print. The experimental results show that our proposed method can work well.

Keywords Gabor multi-manifold discriminant analysis (MMDA) feature extraction finger-knuckle-print image recognition

Corresponding Author(s): YANG Wankou,Email:wankou yang@yahoo.com.cn

Issue Date: 05 June 2011

Cite this article:

Wankou YANG,Zhenyu WANG,Changyin SUN. Finger-knuckle-print recognition using Gabor feature and MMDA[J]. Front Elect Electr Eng Chin, 0, (): 374-380.

URL:

https://academic.hep.com.cn/fee/EN/10.1007/s11460-011-0141-3
https://academic.hep.com.cn/fee/EN/Y0/V/I/374

1	Zhang D. Automated Biometrics: Technologies and Systems. Dordrecht: Kluwer Academic, 2000
2	Jain A K, Flynn P, Ross A. Handbook of Biometrics. New York: Springer, 2007
3	E-channel system of the Hong Kong government. http://www.immd.gov.hk/ehtml/20041216.htm
4	Woodard D L, Flynn P J. Finger surface as a biometric identifier. Computer Vision and Image Understanding , 2005, 100(3): 357-384 doi: 10.1016/j.cviu.2005.06.003
5	Woodard D L, Flynn P J. Personal identification utilizing finger surface features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2005, 2: 1030-1036
6	Ravikanth C, Kumar A. Biometric authentication using finger-back surface. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 2007, 1-6
7	Zhang L, Zhang L, Zhang D, Zhu H. Online finger-knuckleprint verification for personal authentication. Pattern Recognition , 2010, 43(7): 2560-2571 doi: 10.1016/j.patcog.2010.01.020
8	Liu C, Wechsler H. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing , 2002, 11(4): 467-476 doi: 10.1109/TIP.2002.999679
9	Zhang B, Shan S, Chen X, Gao W. Histogram of Gabor phase patterns (HGPP): a novel object representation approach for face recognition. IEEE Transactions on Image Processing , 2007, 16(1): 57-68 doi: 10.1109/TIP.2006.884956
10	Yang W, Sun C, Zhang L. Face recognition using a multimanifold discriminant analysis method. In: Proceedings of the 20th International Conference on Pattern Recognition . 2010, 527-530 doi: 10.1109/ICPR.2010.134
11	Hamouz M, Kittler J, Kamarainen J K, Paalanen P, K?lvi?inen H, Matas J. Feature-based affine-invariant localization of faces. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2005, 27(9): 1490-1495 doi: 10.1109/TPAMI.2005.179
12	Kamarainen J K, Kyrki V, K?lvi?inen H. Invariance properties of Gabor filter-based features — overview and applications. IEEE Transactions on Image Processing , 2006, 15(5): 1088-1099 doi: 10.1109/TIP.2005.864174
13	Jain A K, Duin R P W, Mao J. Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2000, 22(1): 4-37 doi: 10.1109/34.824819
14	Fukunaga K. Introduction to Statistical Pattern Recognition. 2nd ed. Boston: Academic Press, 1990
15	Raudys S J, Jain A K. Small sample size effects in statistical pattern recognition: recommendations for practitioners. IEEE Transactions on Pattern Ana1ysis and Machine Intelligence , 1991, 13(3): 252-264
16	Li H, Jiang T, Zhang K. Efficient and robust feature extraction by maximum margin criterion. IEEE Transactions on Neural Networks , 2006, 17(1): 157-165 doi: 10.1109/TNN.2005.860852
17	Yang W, Wang J, Ren M, Yang J, Zhang L. Feature extraction based on Laplacian bidirectional maximum margin criterion. Pattern Recognition , 2009, 42(11): 2327-2334 doi: 10.1016/j.patcog.2009.03.017
18	Kw K C, Pedry W. Face recognition using a fuzzy fisherface classifier. Pattern Recognition , 2005, 38(10): 1717-1732 doi: 10.1016/j.patcog.2005.01.018
19	Yang W, Wang J, Ren M, Zhang L, Yang J. Feature extraction using fuzzy inverse FDA. Neurocomputing , 2009, 72(13-15): 3384-3390 doi: 10.1016/j.neucom.2009.03.011
20	Yang W, Yan X, Zhang L, Sun C. Feature extraction based on fuzzy 2DLDA. Neurocomputing , 2010, 73(10-12): 1556-1561 doi: 10.1016/j.neucom.2009.12.025
21	Tenenbaum J B, de Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction. Science , 2000, 290(5500): 2319-2323 doi: 10.1126/science.290.5500.2319
22	Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science , 2000, 290(5500): 2323-2326 doi: 10.1126/science.290.5500.2323
23	He X, Yan S, Hu Y, Niyogi P, Zhang H. Face recognition using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2005, 27(3): 328-340 doi: 10.1109/TPAMI.2005.55
24	Zhao D, Lin Z, Tang X. Laplacian PCA and its applications. In: Proceedings of IEEE International Conference on Computer Vision . 2007, 1-8
25	Zhao D, Liu Z, Xiao R, Tang X. Linear Laplacian discrimination for feature extraction. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition . 2007, 1-7
26	Li B, Huang D S, Wang C, Liu K H. Feature extraction using constrained maximum variance mapping. Pattern Recognition , 2008, 41(11): 3287-3294 doi: 10.1016/j.patcog.2008.05.014
27	Wang H, Chen S, Hu Z, Zheng W. Locality-preserved maximum information projection. IEEE Transactions on Neural Networks , 2008, 19(4): 571-585 doi: 10.1109/TNN.2007.910733
28	Zhang L, Zhang L, Zhang D. Finger-knuckle-print: a new biometric identifier. In: Proceedings of the IEEE International Conference on Image Processing . 2009, 1981-1984
29	Turk M, Pentland A. Eigenfaces for recognition. Journal of Cognitive Neuroscience , 1991, 3(1): 71-86 doi: 10.1162/jocn.1991.3.1.71
30	Belhumeur V, Hespanha J, Kriegman D. Eigenfaces vs Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Ana1ysis and Machine Intelligence , 1997, 19(7): 711-720
31	Ojala T, Pietikainen M, Maenpaa T. Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2002, 24(7): 971-987 doi: 10.1109/TPAMI.2002.1017623

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