CLC number: TP391
On-line Access: 2010-07-06
Received: 2009-09-05
Revision Accepted: 2010-02-01
Crosschecked: 2010-06-09
Cited: 14
Clicked: 9191
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.
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