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Front. Inform. Technol. Electron. Eng.  2010, Vol. 11 Issue (7): 514-524    DOI: 10.1631/jzus.C0910550
    
Finger vein recognition using weighted local binary pattern code based on a support vector machine  
Hyeon Chang Lee1, Byung Jun Kang2, Eui Chul Lee3, Kang Ryoung Park*,1
1 Division of Electronics and Electrical Engineering, Dongguk University, Seoul 100-715, Korea 2 Electronics and Telecommunications Research Institute, Daejeon 305-700, Korea 3 Division of Fusion and Convergence of Mathematical Sciences, the National Institute for Mathematical Sciences, Daejeon 305-340, Korea
Finger vein recognition using weighted local binary pattern code based on a support vector machine
Hyeon Chang Lee1, Byung Jun Kang2, Eui Chul Lee3, Kang Ryoung Park*,1
1 Division of Electronics and Electrical Engineering, Dongguk University, Seoul 100-715, Korea 2 Electronics and Telecommunications Research Institute, Daejeon 305-700, Korea 3 Division of Fusion and Convergence of Mathematical Sciences, the National Institute for Mathematical Sciences, Daejeon 305-340, Korea
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摘要： 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.
关键词： Finger vein recognition;  Support vector machine (SVM);  Weight;  Local binary pattern (LBP)    
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.
Key words: Finger vein recognition    Support vector machine (SVM)    Weight    Local binary pattern (LBP)
收稿日期: 2009-09-05 出版日期: 2010-07-06
CLC:  TP391  
基金资助: Project (No. R112002105070020(2010)) supported by the National Research  Foundation  of  Korea  (NRF)  through  the  Biometrics  Engi-
neering Research Center (BERC) at Yonsei University
通讯作者: Kang Ryoung PARK     E-mail: parkgr@dongguk.edu
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引用本文:

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. Front. Inform. Technol. Electron. Eng., 2010, 11(7): 514-524.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C0910550    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2010/V11/I7/514

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