Gender classification using principal geodesic analysis and Gaussian mixture models

被引：0

作者：

Wu, Jing ^{[1
]}

Smith, William A. P. ^{[1
]}

Hancock, Edwin 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 | 2006年 / 4225卷

关键词：

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：

The aim in this paper is to show how to discriminate gender using a parameterized representation of fields of facial surface normals (needle-maps) which can be extracted from 2D intensity images using shape-from-shading (SFS). We makes use of principle geodesic analysis (PGA) to parameterize the facial needle-maps. Using feature selection, we determine which of the components of the resulting parameter vector are the most significant in distinguishing gender. Using the EM algorithm we distinguish gender by fitting a two component mixture model to the vectors of selected features. Results on real-world data reveal that the method gives gender discrimination results that are comparable to human observers.

引用

页码：58 / 67

页数：10

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