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Facial gender classification using shape-from-shading

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Publication details

JournalImage and Vision Computing
DatePublished - Jun 2010
Issue number6
Volume28
Number of pages10
Pages (from-to)1039-1048
Original languageEnglish

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.

Bibliographical note

(C) 2009 Elsevier B.V. All rights reserved.

    Research areas

  • Gender classification, Principal geodesic analysis, Shape-from-shading, PRINCIPAL GEODESIC ANALYSIS, FACES, CONSTRAINTS, DIFFERENCE, STATISTICS, IMAGES

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