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

Chang LEE; Jun KANG; Chul LEE; Ryoung PARK

doi:10.1631/jzus.C0910550

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

计算机与自动化 | Updated：2022-08-31

- Finger vein recognition using weighted local binary pattern code based on a support vector machine
- Finger vein recognition using weighted local binary pattern code based on a support vector machine
- 信息与电子工程前沿（英文） 2010年11卷第7期页码：514-524
- Affiliations：
  
  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
- Author bio：
  
  ‡Corresponding Author
- Funds：
- DOI：10.1631/jzus.C0910550
  中图分类号：
- 收稿：2009-09-05，
  
  修回：2010-06-09，
  
  录用：2010-02-01，
  
  纸质出版：2010-07
- Accepted：
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Finger vein recognition using weighted local binary pattern code based on a support vector machine[J]. 信息与电子工程前沿（英文）, 2010,11(7):514-524.

Chang LEE, Jun KANG, Chul LEE, et al. Finger vein recognition using weighted local binary pattern code based on a support vector machine[J]. Frontiers of Information Technology & Electronic Engineering, 2010, 11(7): 514-524.
Finger vein recognition using weighted local binary pattern code based on a support vector machine[J]. 信息与电子工程前沿（英文）, 2010,11(7):514-524. DOI： 10.1631/jzus.C0910550.

Chang LEE, Jun KANG, Chul LEE, et al. Finger vein recognition using weighted local binary pattern code based on a support vector machine[J]. Frontiers of Information Technology & Electronic Engineering, 2010, 11(7): 514-524. DOI： 10.1631/jzus.C0910550.

摘要

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

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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