Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Accumulating weighted segmentation in 3D face recognition

Accumulating weighted segmentation in 3D face recognition 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Wireless and Mobile Computing Inderscience Publishers

Accumulating weighted segmentation in 3D face recognition

Loading next page...
 
/lp/inderscience-publishers/accumulating-weighted-segmentation-in-3d-face-recognition-9ZK3L4hcLd

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1741-1084
eISSN
1741-1092
DOI
10.1504/ijwmc.2022.122488
Publisher site
See Article on Publisher Site

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.

Journal

International Journal of Wireless and Mobile ComputingInderscience Publishers

Published: Jan 1, 2022

There are no references for this article.