CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2010-06-09
Cited: 14
Clicked: 9499
Hyeon Chang Lee, Byung Jun Kang, Eui Chul Lee, Kang Ryoung Park. Finger vein recognition using weighted local binary pattern code based on a support vector machine[J]. Journal of Zhejiang University Science C, 2010, 11(7): 514-524.
@article{title="Finger vein recognition using weighted local binary pattern code based on a support vector machine",
author="Hyeon Chang Lee, Byung Jun Kang, Eui Chul Lee, Kang Ryoung Park",
journal="Journal of Zhejiang University Science C",
volume="11",
number="7",
pages="514-524",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C0910550"
}
%0 Journal Article
%T Finger vein recognition using weighted local binary pattern code based on a support vector machine
%A Hyeon Chang Lee
%A Byung Jun Kang
%A Eui Chul Lee
%A Kang Ryoung Park
%J Journal of Zhejiang University SCIENCE C
%V 11
%N 7
%P 514-524
%@ 1869-1951
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C0910550
TY - JOUR
T1 - Finger vein recognition using weighted local binary pattern code based on a support vector machine
A1 - Hyeon Chang Lee
A1 - Byung Jun Kang
A1 - Eui Chul Lee
A1 - Kang Ryoung Park
J0 - Journal of Zhejiang University Science C
VL - 11
IS - 7
SP - 514
EP - 524
%@ 1869-1951
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C0910550
Abstract: finger vein recognition is a biometric technique which identifies individuals using their unique finger vein patterns. It is reported to have a high accuracy and rapid processing speed. In addition, it is impossible to steal a vein pattern located inside the finger. We propose a new identification method of finger vascular patterns using a weighted local binary pattern (LBP) and support vector machine (SVM). This research is novel in the following three ways. First, holistic codes are extracted through the LBP method without using a vein detection procedure. This reduces the processing time and the complexities in detecting finger vein patterns. Second, we classify the local areas from which the LBP codes are extracted into three categories based on the SVM classifier: local areas that include a large amount (LA), a medium amount (MA), and a small amount (SA) of vein patterns. Third, different weights are assigned to the extracted LBP code according to the local area type (LA, MA, and SA) from which the LBP codes were extracted. The optimal weights are determined empirically in terms of the accuracy of the finger vein recognition. Experimental results show that our equal error rate (EER) is significantly lower compared to that without the proposed method or using a conventional method.
[1]Ahonen, T., Hadid, A., Pietikäinen, M., 2006. Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell., 28(12):2037-2041.
[2]Choi, J.H., Song, W.S., Kim, T.J., Lee, S.R., Kim, H.C., 2009. Finger vein extraction using gradient normalization and principal curvature. SPIE, 7251:1-9.
[3]Ding, Y., Zhuang, D., Wang, K., 2005. A Study of Hand Vein Recognition Method. Proc. IEEE Int. Conf. on Mechatronics and Automation, p.2106-2110.
[4]Dubuisson, M.P., Jain, A.K., 1994. A Modified Hausdorff Distance for Object Matching. Proc. 12th Int. Conf. on Pattern Recognition, p.566-568.
[5]Ferrer, M.A., Morales, A., Ortega, L., 2009. Infrared hand dorsum images for identification. Electron. Lett., 45(6):306-308.
[6]Guo, G., Jones, M.J., 2008. Iris Extraction Based on Intensity Gradient and Texture Difference. Proc. IEEE Workshop on Applications of Computer Vision, p.1-6.
[7]Huttenlocher, D.P., Klanderman, G.A., Rucklidge, W.A., 1993. Comparing images using the Hausdorff distance. IEEE Trans. Pattern Anal. Mach. Intell., 15(9):850-863.
[8]Jain, A.K., Ross, A., Prabhakar, S., 2004. An introduction to biometric recognition. IEEE Trans. Circ. Syst. Video Technol., 14(1):4-19.
[9]Jang, J., Park, K.R., Kim, J., Lee, Y., 2008. New focus assessment method for iris recognition systems. Pattern Recogn. Lett., 29(13):1759-1767.
[10]Jang, Y.K., Kang, B.J., Park, K.R., 2008. A study on touchless finger vein recognition robust to the alignment and rotation of finger. KIPS Trans. Part B, 15-B(4):275-284.
[11]Kang, B.J., Park, K.R., 2009. Multimodal biometric authentication based on the fusion of finger vein and finger geometry. Opt. Eng., 48(9):090501.
[12]Lee, E.C., Park, K.R., 2009. Restoration method of skin scattering blurred vein image for finger vein recognition. Electron. Lett., 45(21):1074-1076.
[13]Lee, E.C., Lee, H.C., Park, K.R., 2009. Finger vein recognition by using minutia based alignment and local binary pattern-based feature extraction. Int. J. Imag. Syst. Technol., 19(3):179-186.
[14]Lin, C., Fan, K., 2004. Biometric verification using thermal images of palm-dorsa vein patterns. IEEE Trans. Circ. Syst. Video Technol., 14(2):199-213.
[15]Miura, N., Nagasaka, A., Miyatake, T., 2004. Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Mach. Vis. Appl., 15(4):194-203.
[16]Miura, N., Nagasaka, A., Miyatake, T., 2007. Extraction of finger-vein patterns using maximum curvature points in image profiles. IEICE Trans. Inform. Syst., E90-D(8):1185-1194.
[17]Ojala, T., Pietikäinen, M., Harwood, D., 1996. A comparative study of texture measures with classification based on featured distributions. Pattern Recog., 29(1):51-59.
[18]Ojala, T., Pietikäinen, M., Maenpaa, T., 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell., 24(7):971-987.
[19]Solla, S.A., Leen, T.K., Müller, K., 2000. Advances in Neural Information Processing Systems 12. MIT Press, Cambridge, p.834-840.
[20]Sukumaran, S., Punithavalli, M., 2009. Retina recognition based on fractal dimension. Int. J. Comput. Sci. Network Secur., 9(10):66-70.
[21]Usher, D., Tosa, Y., Friedman, M., 2008. Ocular Biometrics: Simultaneous Capture and Analysis of the Retina and Iris. Advances in Biometrics. Springer, London.
[22]Vapnik, V., 1998. Statistical Learning Theory. John Wiley & Sons, New York.
[23]Wang, L., Leedham, G., 2006. Near- and Far-Infrared Imaging for Vein Pattern Biometrics. Proc. IEEE Int. Conf. on Video and Signal Based Surveillance, p.52.
[24]Wang, L., Leedham, G., Cho, D.S.Y., 2008. Minutiae feature analysis for infrared hand vein pattern biometrics. Pattern. Recog., 41(3):920-929.
[25]Watanabe, M., 2008. Palm Vein Authentication. Advances in Biometrics. Springer, London.
[26]Yanagawa, T., Aoki, S., Ohyama, T., 2007. Human Finger Vein Images Are Diverse and Its Patterns Are Useful for Personal Identification. MHF Preprint Series, MHF 2007-12, 21st Century COE Program, Development of Dynamic Mathematics with High Functionality, Kyushu University, Korea.
[27]Yang, H., Wang, Y., 2007. A LBP-Based Face Recognition Method with Hamming Distance Constraint. Proc. 4th Int. Conf. on Image and Graphics, p.645-649.
[28]Zhang, Z., Ma, S., Han, X., 2006. Multiscale Feature Extraction of Finger-Vein Patterns Based on Curvelets and Local Interconnection Structure Neural Network. Proc. 18th Int. Conf. on Pattern Recognition, p.145-148.
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