Gender Classification using Shape from Shading

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 improve the performance of gender classification. We incorporate principal geodesic analysis (PGA) into SFS to guarantee the recovered needle-maps is a possible example defined by a statistical model. Because the recovered facial needlemaps satisfy data-closeness constraint, they not only give the facial shape information, but also combine the image intensity implicitly. Experiments show that this combination gives better gender classification performance than using facial shape or texture information alone.