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Facial gender classification using shape-from-shading

Wu, Jing, Smith, W. A. P. and Hancock, E. R. 2010. Facial gender classification using shape-from-shading. Image and Vision Computing 28 (6) , pp. 1039-1048. 10.1016/j.imavis.2009.09.003

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Abstract

The aim in this paper is to show how to use the 2.5D facial surface normals (needle-maps) recovered using shape-from-shading (SFS) to perform gender classification. We use principal geodesic analysis (PGA) to model the distribution of facial surface normals which reside on a Remannian manifold. We incorporate PGA into shape-from-shading, and develop a principal geodesic shape-from-shading (PGSFS) method. This method guarantees that the recovered needle-maps exhibit realistic facial shape by satisfying a statistical model. Moreover, because the recovered facial needle-maps satisfy the data-closeness constraint as a hard constraint, they not only encode facial shape but also implicitly encode image intensity. Experiments explore the gender classification performance using the recovered facial needle-maps on two databases (Notre Dame and FERET), and compare the results with those obtained using intensity images. The results demonstrate the feasibility of gender classification using the recovered facial shape information.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords: Gender classification; Principal geodesic analysis; Shape-from-shading
Publisher: Elsevier
ISSN: 0262-8856
Date of Acceptance: 14 September 2009
Last Modified: 04 Jun 2017 09:03
URI: http://orca.cf.ac.uk/id/eprint/89861

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