Weighted principal geodesic analysis for facial gender classification

被引：0
|
作者：
Wu, Jing [1 ]
Smith, W. A. P. [1 ]
Hancock, E. R. [1 ]
机构：
[1] Univ York, Dept Comp Sci, York YO10 5DD, N Yorkshire, England
来源：
PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS AND APPLICATIONS, PROCEEDINGS | 2007年 / 4756卷
关键词：
gender classification; facial surface normals; principal geodesic analysis; weighted principal geodesic analysis; gender discriminating power;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we describe a weighted principal geodesic analysis (WPGA) method to extract features for gender classification based on 2.5D facial surface normals (needle-maps) which can be extracted from 2D intensity images using shape-from-shading (SFS). By incorporating the weight matrix into principal geodesic analysis (PGA), we control the obtained principal axis to be in the direction of the variance on gender information. Experiments show that using WPGA, the leading eigenvectors encode more gender discriminating power than using PGA, and that gender classification based on leading WPGA parameters is more accurate and stable than based on leading PGA parameters.
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页码：331 / 339
页数：9
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