Research on 3D face recognition method in cloud environment based on semi supervised clustering algorithm

2016 ◽  
Vol 76 (16) ◽  
pp. 17055-17073 ◽  
Author(s):  
Cuixia Li ◽  
Yingjun Tan ◽  
Dingbiao Wang ◽  
Peijie Ma
2012 ◽  
Vol 29 ◽  
pp. 705-709 ◽  
Author(s):  
Bi Kun ◽  
Luo Lin ◽  
Zhao Li ◽  
Fang Shi Liang

2021 ◽  
pp. 306-314
Author(s):  
Liangliang Shi ◽  
◽  
Xia Wang ◽  
Yongliang Shen

In order to improve the accuracy and speed of 3D face recognition, this paper proposes an improved MB-LBP 3D face recognition method. First, the MB-LBP algorithm is used to extract the features of 3D face depth image, then the average information entropy algorithm is used to extract the effective feature information of the image, and finallythe Support Vector Machine algorithm is used to identify the extracted effective information. The recognition rate on the Texas 3DFRD database is 96.88%, and the recognition time is 0.025s. The recognition rate in the self-made depth library is 96.36%, and the recognition time is 0.02s.It can be seen from the experimental results that the algorithm in this paper has better performance in terms of accuracy and speed.


2018 ◽  
Vol 39 (4) ◽  
pp. 41-45 ◽  
Author(s):  
Cai Chuanli ◽  
Zhang Jianping ◽  
Zhang Yanbo

Optik ◽  
2020 ◽  
Vol 220 ◽  
pp. 165157 ◽  
Author(s):  
Liangliang Shi ◽  
Xia Wang ◽  
Yongliang Shen

10.5772/58251 ◽  
2014 ◽  
Vol 11 (3) ◽  
pp. 36 ◽  
Author(s):  
Patrik Kamencay ◽  
Robert Hudec ◽  
Miroslav Benco ◽  
Martina Zachariasova

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