Gender classification based on facial surface normals
Jing Wu, William A. P. Smith, Edwin R. Hancock · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · 2008
In this paper, we perform gender classification based on 2.5D facial surface normals (facial needle-maps), and present two novel principal geodesic analysis (PGA) methods, weighted PGA and supervised PGA, to parameterize the facial needle-maps, and compare their performances with PGA for gender classification. Experimental results demonstrate the feasibility of gender classification based on facial needle-maps, and show that incorporating weights or pairwise relationships of labeled data into PGA improves the gender discriminating powers in the leading eigenvectors and the gender classification accuracy.