EXTRACTING GENDER DISCRIMINATING FEATURES FROM FACIAL NEEDLE-MAPS

被引：2

作者：

Wu, Jing ^{[1
]}

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

Hancock, E. R. ^{[1
]}

Kawulok, Michal ^{[2
]}

机构：

[1] Univ York, Dept Comp Sci, York YO10 5DD, N Yorkshire, England

[2] Silesian Tech Univ, Inst Comp Sci, P-44100 Gliwice, Poland

来源：

2009 16TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-6 | 2009年

关键词：

3D image processing; feature extraction; gender classification; PRINCIPAL GEODESIC ANALYSIS; RECOGNITION;

D O I：

10.1109/ICIP.2009.5414129

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 show how to extract gender discriminating features from 2.5D facial needle-maps. The standard eigenspace analysis method for non-Euclidean data is principal geodesic analysis (PGA). Based on PGA, we propose a novel supervised weighted PGA method which incorporates local weights into standard PGA to improve gender discriminating capability of the extracted features: The weight map is iteratively optimized from the labeled data, which is different from other gender relevant weights used in the literature [1]. Experimental results illustrate the effectiveness of this method and its successful application to gender classification.

引用

页码：2449 / +

页数：2

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