Accumulating weighted segmentation in 3D face recognition. (24th April 2022)
- Record Type:
- Journal Article
- Title:
- Accumulating weighted segmentation in 3D face recognition. (24th April 2022)
- Main Title:
- Accumulating weighted segmentation in 3D face recognition
- Authors:
- Ju, Quan
Hu, Haitao
Wang, Yingfeng - 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.
- Is Part Of:
- International journal of wireless and mobile computing. Volume 22:Number 1(2022)
- Journal:
- International journal of wireless and mobile computing
- Issue:
- Volume 22:Number 1(2022)
- Issue Display:
- Volume 22, Issue 1 (2022)
- Year:
- 2022
- Volume:
- 22
- Issue:
- 1
- Issue Sort Value:
- 2022-0022-0001-0000
- Page Start:
- 84
- Page End:
- 92
- Publication Date:
- 2022-04-24
- Subjects:
- face segmentation -- expression variation -- accumulating weight -- face recognition in 3D
Mobile computing -- Periodicals
Wireless communication systems -- Periodicals
004.6 - Journal URLs:
- http://www.inderscience.com/info/inissues.php?jcode=ijwmc ↗
http://www.inderscience.com/ ↗ - Languages:
- English
- ISSNs:
- 1741-1084
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 20177.xml