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Weighted principal geodesic analysis for facial gender classification

Wu, Jing, Smith, W. A. P. and Hancock, E. R. 2008. Weighted principal geodesic analysis for facial gender classification. Presented at: 12th Iberoamericann Congress on Pattern Recognition, CIARP 2007, Valparaiso, Chile, 13-16 November 2007. Published in: Rueda, Luis, Mery, Domingo and Kittler, Josef eds. Progress in Pattern Recognition, Image Analysis and Applications: 12th Iberoamericann Congress on Pattern Recognition, CIARP 2007, Valparaiso, Chile, November 13-16, 2007. Proceedings. Lecture Notes in Computer Science , vol. 4756. Berlin Heidelberg: Springer, pp. 331-339. 10.1007/978-3-540-76725-1_35

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Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: Springer
ISBN: 9783540767244
ISSN: 0302-9743
Last Modified: 04 Jun 2017 09:03
URI: http://orca.cf.ac.uk/id/eprint/89870

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