Finger-knuckle-print recognition using Gabor feature and MMDA

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

References

[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

[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

[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

[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

[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

[11] Hamouz M, Kittler J, Kamarainen J K, Paalanen P, Kälviäinen H, Matas J. Feature-based affine-invariant localization of faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(9): 1490-1495

[12] Kamarainen J K, Kyrki V, Kälviäinen H. Invariance properties of Gabor filter-based features — overview and applications. IEEE Transactions on Image Processing, 2006, 15(5): 1088-1099

[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

[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

[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

[18] Kw K C, Pedry W. Face recognition using a fuzzy fisherface classifier. Pattern Recognition, 2005, 38(10): 1717-1732

[19] Yang W, Wang J, Ren M, Zhang L, Yang J. Feature extraction using fuzzy inverse FDA. Neurocomputing, 2009, 72(13-15): 3384-3390

[20] Yang W, Yan X, Zhang L, Sun C. Feature extraction based on fuzzy 2DLDA. Neurocomputing, 2010, 73(10-12): 1556-1561

[21] Tenenbaum J B, de Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction. Science, 2000, 290(5500): 2319-2323

[22] Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000, 290(5500): 2323-2326

[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

[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

[27] Wang H, Chen S, Hu Z, Zheng W. Locality-preserved maximum information projection. IEEE Transactions on Neural Networks, 2008, 19(4): 571-585

[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

[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

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg