Dorsal hand vein recognition system based on maximum circular region of interest

被引：0

作者：

Liu F. ^{[1
]}

Jiang S.-K. ^{[1
]}

Kang B. ^{[1
]}

Hou T. ^{[1
]}

机构：

[1] College of Communication Engineering, Jilin University, Changchun

来源：

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2020年 / 50卷 / 06期

关键词：

Dorsal hand vein recognition; Hand vein database; Information processing technology; Maximum inscribed circle; Region of interest extraction;

D O I：

10.13229/j.cnki.jdxbgxb20190777

中图分类号：

学科分类号：

摘要：

Region of Interest (ROI) extraction is a critical step in dorsal hand vein recognition, which directly affects the performance of recognition system. However, the existing circular positioning algorithms are time-consuming and low rotation invariance. To overcome these problems, we proposed a dorsal hand vein recognition system based on maximum inscribed circular ROI. The system extracted ROI using Delaunay triangulation algorithm to reduce time complexity and locate the little thumb point to improve rotation invariance. 3680 hand vein images were collected to verify the validity of the proposed system. Experimental results demonstrate that the system is effective and the mean extraction time is 6.1 ms, the mean recognition rate reaches 98.26%. © 2020, Jilin University Press. All right reserved.

引用

页码：2191 / 2196

页数：5

共 29 条

[11]

Trabelsi R B, Masmoudi A D, Masmoudi D S., Hand vein recognition system with circular difference and statistical directional patterns based on an artificial neural network, Multimedia Tools and Applications, 75, 2, pp. 687-707, (2016)

[12]

Liu Fu, Zong Yu-xuan, Kang Bing, Et al., Dorsal hand vein recognition system based on optimized texture features, Journal of Jilin University(Engineering and Technology Edition), 48, 6, pp. 1844-1850, (2018)

[13]

Lee J C, Lee C H, Hsu C B, Et al., Dorsal hand vein recognition based on 2D Gabor filters, Imaging Science Journal, 62, 3, pp. 127-138, (2014)

[14]

Meng Z H, Gu X D., Hand vein identification using local Gabor ordinal measure, Journal of Electronic Imaging, 23, 5, (2014)

[15]

Wang Y D, Duan Q Y, Shark L K, Et al., Improving hand vein recognition by score weighted fusion of wavelet-domain multi-radius local binary patterns, International Journal of Computer Applications in Technology, 54, 3, pp. 151-160, (2016)

[16]

Trabelsi R B, Masmoudi A D, Masmoudi D S., A novel biometric system based hand vein recognition, Journal of Testing and Evaluation, 42, 4, pp. 809-818, (2014)

[17]

Anton F, Mioc D, Gold C., The voronoi diagram of circles made easy, 4th International Symposium on Voronoi Diagrams in Science and Engineering (ISVD 2007), pp. 15-24, (2007)

[18]

Ojala T, Pietikainen M, Maenpaa T., Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 7, pp. 971-987, (2002)

[19]

He K M, Zhang X Y, Ren S Q, Et al., Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 9, pp. 1904-1916, (2015)

[20]

Shan Li-yan, Li Xin-wei, Video fingerprinting algorithm based on temporal and spatial information feature fusion, Computer Engineering, 45, 8, pp. 260-265, (2019)

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