Volume 16 Number 6 (Nov. 2021)
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JSW 2021 Vol.16(6): 306-314 ISSN: 1796-217X
doi: 10.17706/jsw.16.6.306-314

Research on an Improved MB-LBP 3D Face Recognition Method

Liangliang Shi, Xia Wang, Yongliang Shen*
College of Electronic Engineering, Heilongjiang University, Harbin,Heilongjiang, China.

Abstract—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.

Index Terms—Average information entropy, depth data, MB-LBP, Support vector machine,3D face recognition.


Cite: Liangliang Shi, Xia Wang, Yongliang Shen, "Research on an Improved MB-LBP 3D Face Recognition Method," Journal of Software vol. 16, no. 6, pp. 306-314, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0)

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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