Personal Identification Using Finger-Knuckle-Print Based on Local Binary Pattern

Abstract:

Over the last ten years, considerable progress has been made on the new hand-based biometric recognition, such as palmprint and hand vein. During this period, it has been proved that Finger-Knuckle-Print (FKP) can be used as a biometric identifier. In this paper, we present an effective FKP identification method based on Local Binary Pattern (LBP), whose idea is to divide the region of interest (ROI) of FKP into a set of sub-image blocks, which can be applied to extract the local features of the FKP. After that, LBP histograms of image blocks in a FKP ROI image are connected together to build the feature vector of the FKP ROI image. In the match stage, histogram intersection distance is applied as the similarity measurement between sample and template. Experimental results conducted on a database of 165 persons (4 fingers per person) show that the proposed method is effective.

• Periodical: Applied Mechanics and Materials (Volume 441)
• Pages: 703-706
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.441.703
• Online since: December 2013
• Authors: Peng Fei Yu, Hao Zhou, Hai Yan Li
• Keywords: Biometrics, Finger-Knuckle-Print, LBP, Local Binary Pattern (LBP)

References

[1] D. Maltoni, D. Maio, A.K. Jain, S. Prabhakar, Handbook of Fingerprint Recognition[M], Springer, (2003).

[2] N. Ratha, R. Bolle, Automatic Fingerprint Recognition Systems[M], Springer, (2004).

[3] D. Zhang, W.K. Kong, J. You, M. Wong, Online Palmprint Identification[J], IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25 (9): 1041–1050.

DOI: 10.1109/tpami.2003.1227981

[4] A. Kong, D. Zhang, M. Kamel. A Survey of Palmprint Recognition[J]. Pattern Recognition, 2009, 42(7): 1408-1418.

DOI: 10.1016/j.patcog.2009.01.018

[5] Y. Ding, D. Zhuang, K. Wang. A Study of Hand Vein Recognition Method[C], IEEE International Conference on Mechatronics and Automation, 2005. IEEE, 2005, 4: 2106-2110.

DOI: 10.1109/icma.2005.1626888

[6] L. Wang, G. Leedham, D. Siu-Yeung Cho. Minutiae Feature Analysis for Infrared Hand Vein Pattern Biometrics[J]. Pattern Recognition, 2008, 41(3): 920-929.

DOI: 10.1016/j.patcog.2007.07.012

[7] D.L. Woodard, P.J. Flynn, Finger Surface as a Biometric Identifier[J], Computer Vision and Image Understanding, 2005, 100 (3): 357–384.

DOI: 10.1016/j.cviu.2005.06.003

[8] A. Kumar, C. Ravikanth, Personal Authentication Using Finger Knuckle Surface[J], IEEE Transactions on Information Forensics and Security, 2009, 4 (1): 98–109.

DOI: 10.1109/tifs.2008.2011089

[9] L. Zhang, L. Zhang, D. Zhang, H. Zhu, Online Finger–Knuckle-Print Verification for Personal Authentication[J], Pattern Recognition, 2010, 43 (7): 2560–2571.

DOI: 10.1016/j.patcog.2010.01.020

[10] G. S. Badrinath, A. Nigam, P. Gupta. An Efficient Finger-Knuckle-Print Based Recognition System Fusing Sift and Surf Matching Scores[J]. Information and Communications Security. Springer Berlin Heidelberg, 2011: 374-387.

DOI: 10.1007/978-3-642-25243-3_30

[11] L. Zhang, L. Zhang, D. Zhang, H. Zhu. Ensemble of Local and Global Information for Finger–Knuckle-Print Recognition[J]. Pattern Recognition, 2011, 44(9): 1990-(1998).

DOI: 10.1016/j.patcog.2010.06.007

[12] L. Zhang, H. Li. Encoding local image patterns using Riesz transforms: With Applications to Palmprint and Finger-Knuckle-Print Recognition[J]. Image and Vision Computing, 2012, 30(12): 1043-1051.

DOI: 10.1016/j.imavis.2012.09.003

[13] T. Ojala, M. Pietikainen, T. Maeopaa. Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7), 971–987.

DOI: 10.1109/tpami.2002.1017623

[14] T. Ojala, M. Pietikainen, D. Harwood. A Comparative Study of Texture Measures with Classification Based on Feature Distributions. Pattern Recognition, 1996, 29(1), 51–59.

DOI: 10.1016/0031-3203(95)00067-4