Deep Patch Matching For Hand Vein Recognition

Author(s):  
Qi Yang ◽  
Danni Ai ◽  
Hong Song ◽  
Yong Huang ◽  
Yongtian Wang ◽  
...  
2013 ◽  
Vol 462-463 ◽  
pp. 312-315
Author(s):  
Cai Xia Liu

Biometrics technology is an important security technology and the research of it has become a new hot spot for its superior security features. Then hand vein recognition is a new biological feature recognition which has many advantages, such as safety, non-contact. According to the features of human hand vein image, a hand vein preprocessing method based on wavelet transform and windows maximum between-class difference method threshold (OTSU) segmentation algorithm is proposed. In this paper, the hand vein image is enhanced by adaptive histogram equalization in low frequency part of the hand vein image after wavelet decomposition and filtering before feature extraction. Then the windows OTSU threshold segmentation algorithm is used to get the features. The experimental results show that this method is simple and easy to realize and has laid a good foundation for the latter part of the vein recognition.


2014 ◽  
Vol 43 (1) ◽  
pp. 110004
Author(s):  
胡云朋 HU Yun-peng ◽  
王志勇 WANG Zhi-yong ◽  
李飞 LI Fei ◽  
杨晓苹 YANG Xiao-ping ◽  
薛玉明 XUE Yu-ming

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3718 ◽  
Author(s):  
Yiding Wang ◽  
Heng Cao ◽  
Xiaochen Jiang ◽  
Yuanyan Tang

The dorsal hand vein images captured by cross-device may have great differences in brightness, displacement, rotation angle and size. These deviations must influence greatly the results of dorsal hand vein recognition. To solve these problems, the method of dorsal hand vein recognition was put forward based on bit plane and block mutual information in this paper. Firstly, the input gray image of dorsal hand vein was converted to eight-bit planes to overcome the interference of brightness inside the higher bit planes and the interference of noise inside the lower bit planes. Secondly, the texture of each bit plane of dorsal hand vein was described by a block method and the mutual information between blocks was calculated as texture features by three kinds of modes to solve the problem of rotation and size. Finally, the experiments cross-device were carried out. One device was used to be registered, the other was used to recognize. Compared with the SIFT (Scale-invariant feature transform, SIFT) algorithm, the new algorithm can increase the recognition rate of dorsal hand vein from 86.60% to 93.33%.


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