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On-line Access: 2010-07-06

Received: 2009-09-05

Revision Accepted: 2010-02-01

Crosschecked: 2010-06-09

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Journal of Zhejiang University SCIENCE C 2010 Vol.11 No.7 P.514-524

http://doi.org/10.1631/jzus.C0910550


Finger vein recognition using weighted local binary pattern code based on a support vector machine


Author(s):  Hyeon Chang Lee, Byung Jun Kang, Eui Chul Lee, Kang Ryoung Park

Affiliation(s):  Division of Electronics and Electrical Engineering, Dongguk University, Seoul 100-715, Korea, Electronics and Telecommunications Research Institute, Daejeon 305-700, Korea, Division of Fusion and Convergence of Mathematical Sciences, the National Institute for Mathematical Sciences, Daejeon 305-340, Korea

Corresponding email(s):   parkgr@dongguk.edu

Key Words:  Finger vein recognition, Support vector machine (SVM), Weight, Local binary pattern (LBP)


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.

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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",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C0910550"
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%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
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%P 514-524
%@ 1869-1951
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C0910550

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T1 - Finger vein recognition using weighted local binary pattern code based on a support vector machine
A1 - Hyeon Chang Lee
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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
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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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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