Journal of Jilin University(Engineering and Technology Edition) ›› 2018, Vol. 48 ›› Issue (6): 1844-1850.doi: 10.13229/j.cnki.jdxbgxb20170728

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Dorsal hand vein recognition system based on optimized texture features

LIU Fu1,2(),ZONG Yu-xuan1,2,KANG Bing2,ZHANG Yi-meng3,LIN Cai-xia4,ZHAO Hong-wei5()   

  1. 1. National Key Laboratory for Automotive Simulation and Control, Jilin University, Changchun 130022,China
    2. College of Communication Engineering,Jilin University, Changchun 130022,China
    3. Electronic and Electrical Engineering Department,The University of Sheffield, Sheffield, S10 2TN, UK
    4. College of Information Science and Technology, Hainan University, Haikou 570228, China
    5. College of Computer Science and Technology,Jilin University,Changchun 130012,China
  • Received:2017-07-11 Online:2018-11-20 Published:2018-12-11

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Abstract:

In order to extract and match the texture features of dorsal hand vein, this paper presents a dorsal hand vein recognition system based on optimized texture features. First, we design a image acquisition device to collect vein images. Then, after image preprocessing and three-layer haar wavelet decomposition, we use different scales and directions of gabor kernel function to extract texture features of low-frequency sub-band images. Finally, PCA is used to reduce the dimensionality of features and the nearest neighbor classifier based on Euclidean distance is used to match the features. In this paper, a dorsal hand vein database of Jilin University is established. The experimental results show that the proposed recognition system can effectively improve the identification speed of features and the recognition rate can reach 98.5%. Good efforts on recognition accuracy and efficiency are achieved by the system.

Key words: computer application, dorsal hand vein recognition system, texture feature optimization, vein acquisition device

CLC Number: 

  • TP391.4

Fig.1

Device three-dimensional structure"

Fig.2

Hand vein images"

Fig.3

Image ROI extraction"

Fig.4

Three layers of wavelet decomposition"

Fig.5

Hand vein image database"

Fig.6

Block diagram of identification system"

Fig.7

Recognition rate under different scale coefficients"

Fig.8

Recognition rate under different direction coefficients"

Table 1

Influence of PCA dimensionality reduction dimension k on recognition rate"

降维维数k 贡献率/% 识别率/%
50 88.63 93.0
100 97.91 96.5
150 99.39 98.0
200 99.76 98.5
250 99.89 98.5
300 99.94 98.5

Fig.9

ROC curve"

Table 2

Comparing experimental results before and after optimization"

特征提取方法 维数 识别率/% 识别时间/s
Gabor 230400 95.50 1280
小波分解+Gabor 3600 98.50 19.542
本文方法 200 98.50 1.350

Table 3

Performance of different methods is compared with experimental results"

特征提取方法 识别率/% 特征提取方法 识别率/%
LBP 95.0 2DFLD 95.75
LPQ 96.25 SIFT 96.5
2DPCA 94.5 本文方法 98.5
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