Title: Accumulating weighted segmentation in 3D face recognition

Authors: Quan Ju; Haitao Hu; Yingfeng Wang

Addresses: School of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou, Henan Province, China ' School of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou, Henan Province, China ' School of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou, Henan Province, China

Abstract: In this paper, an accumulating weighted face segmentation approach based on the rigid level of human facial areas is introduced. A mass of 3D face data is measured and analysed to define the most expression-invariant region. Different locations or regions on the human face are observed to have dissimilar invariant levels. Thus, an accumulating weight method is proposed to represent the rigid degree under expression variations. In face identification experiments, performance by employing the accumulating weight is demonstrated to be higher than methods using the expression-invariant region and the full face, respectively. This accumulating weighted face segmentation approach outperforms other state-of-the-art methods in 3D face recognition experiments.

Keywords: face segmentation; expression variation; accumulating weight; face recognition in 3D.

DOI: 10.1504/IJWMC.2022.122488

International Journal of Wireless and Mobile Computing, 2022 Vol.22 No.1, pp.84 - 92

Accepted: 16 Feb 2022
Published online: 27 Apr 2022 *

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