IEICE TRANSACTIONS on Information
Sexual Dimorphism Analysis and Gender Classification in 3D Human Face
Summary :
In this paper, we present the sexual dimorphism analysis in 3D human face and perform gender classification based on the result of sexual dimorphism analysis. Four types of features are extracted from a 3D human-face image. By using statistical methods, the existence of sexual dimorphism is demonstrated in 3D human face based on these features. The contributions of each feature to sexual dimorphism are quantified according to a novel criterion. The best gender classification rate is 94% by using SVMs and Matcher Weighting fusion method. This research adds to the knowledge of 3D faces in sexual dimorphism and affords a foundation that could be used to distinguish between male and female in 3D faces.
- Publication
- IEICE TRANSACTIONS on Information Vol.E93-D No.9 pp.2643-2646
- Publication Date
- 2010/09/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E93.D.2643
- Type of Manuscript
- LETTER
- Category
- Pattern Recognition
Authors
Keyword
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Yuan HU, Li LU, Jingqi YAN, Zhi LIU, Pengfei SHI, "Sexual Dimorphism Analysis and Gender Classification in 3D Human Face" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 9, pp. 2643-2646, September 2010, doi: 10.1587/transinf.E93.D.2643.
Abstract: In this paper, we present the sexual dimorphism analysis in 3D human face and perform gender classification based on the result of sexual dimorphism analysis. Four types of features are extracted from a 3D human-face image. By using statistical methods, the existence of sexual dimorphism is demonstrated in 3D human face based on these features. The contributions of each feature to sexual dimorphism are quantified according to a novel criterion. The best gender classification rate is 94% by using SVMs and Matcher Weighting fusion method. This research adds to the knowledge of 3D faces in sexual dimorphism and affords a foundation that could be used to distinguish between male and female in 3D faces.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.2643/_p
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@ARTICLE{e93-d_9_2643,
author={Yuan HU, Li LU, Jingqi YAN, Zhi LIU, Pengfei SHI, },
journal={IEICE TRANSACTIONS on Information},
title={Sexual Dimorphism Analysis and Gender Classification in 3D Human Face},
year={2010},
volume={E93-D},
number={9},
pages={2643-2646},
abstract={In this paper, we present the sexual dimorphism analysis in 3D human face and perform gender classification based on the result of sexual dimorphism analysis. Four types of features are extracted from a 3D human-face image. By using statistical methods, the existence of sexual dimorphism is demonstrated in 3D human face based on these features. The contributions of each feature to sexual dimorphism are quantified according to a novel criterion. The best gender classification rate is 94% by using SVMs and Matcher Weighting fusion method. This research adds to the knowledge of 3D faces in sexual dimorphism and affords a foundation that could be used to distinguish between male and female in 3D faces.},
keywords={},
doi={10.1587/transinf.E93.D.2643},
ISSN={1745-1361},
month={September},}
Copy
TY - JOUR
TI - Sexual Dimorphism Analysis and Gender Classification in 3D Human Face
T2 - IEICE TRANSACTIONS on Information
SP - 2643
EP - 2646
AU - Yuan HU
AU - Li LU
AU - Jingqi YAN
AU - Zhi LIU
AU - Pengfei SHI
PY - 2010
DO - 10.1587/transinf.E93.D.2643
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2010
AB - In this paper, we present the sexual dimorphism analysis in 3D human face and perform gender classification based on the result of sexual dimorphism analysis. Four types of features are extracted from a 3D human-face image. By using statistical methods, the existence of sexual dimorphism is demonstrated in 3D human face based on these features. The contributions of each feature to sexual dimorphism are quantified according to a novel criterion. The best gender classification rate is 94% by using SVMs and Matcher Weighting fusion method. This research adds to the knowledge of 3D faces in sexual dimorphism and affords a foundation that could be used to distinguish between male and female in 3D faces.
ER -
English
