Supervised principal geodesic analysis on facial surface normals for gender classification

Wu, Jing ORCID: https://orcid.org/0000-0001-5123-9861, Smith, W. A. P. and Hancock, E. R. 2008. Supervised principal geodesic analysis on facial surface normals for gender classification. Presented at: Structural, Syntactic, and Statistical Pattern Recognition Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, 4-6 December 2008. Published in: da Vitoria Lobo, N., Kasparis, T., Georgiopoulos, M., Roli, F., Kwok, J., Anagnostopoulos, G. C. and Loog, M. eds. Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings. Lecture Notes in Computer Science (5342) Berlin Heidelberg: Springer, pp. 664-673. 10.1007/978-3-540-89689-0_70

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Abstract

In this paper, we perform gender classification based on facial surface normals (facial needle-maps). We improve our previous work in [6] by using a non-Lambertian Shape-from-Shading (SFS) method to recover the surface normals, and develop a novel supervised principal geodesic analysis (PGA) to parameterize the facial needle-maps. Experimental results demonstrate the feasibility of gender classification based on facial needle-maps, and shows that incorporating pairwise relationships between the labeled data improves the gender discriminating powers in the leading PGA eigenvectors and gender classification accuracy.

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:	9783540896883
ISSN:	0302-9743
Last Modified:	01 Nov 2022 10:00
URI:	https://orca.cardiff.ac.uk/id/eprint/89866

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