In this paper, we perform gender classification based on facial surface normals (facial needle-maps). We improve our previous work in [6] by using a non-Lambertian Shape-from-Shading (SFS) method to recover the surface normals, and develop a novel supervised principal geodesic analysis (PGA) to parameterize the facial needle-maps. Experimental results demonstrate the feasibility of gender classification based on facial needle-maps, and shows that incorporating pairwise relationships between the labeled data improves the gender discriminating powers in the leading PGA eigenvectors and gender classification accuracy. © 2008 Springer Berlin Heidelberg.
CITATION STYLE
Wu, J., Smith, W. A. P., & Hancock, E. R. (2008). Supervised principal geodesic analysis on facial surface normals for gender classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5342 LNCS, pp. 664–673). https://doi.org/10.1007/978-3-540-89689-0_70
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