PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Facial gender classification using shape-from-shading
Jing Wu, William Smith and Edwin Hancock
Image Vision Comput. Volume 28, Number 6, pp. 1039-1048, 2010. ISSN 0262-8856


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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Machine Vision
Learning/Statistics & Optimisation
ID Code:7373
Deposited By:Edwin Hancock
Deposited On:17 March 2011