Gender categorization based on 3D faces

In this paper, we evaluate the gender classification performance based on 3D faces according to three aspects: image resolution, data fusion and texture descriptor. Our experiments are based on CASIA 3D Face Database, which has 123 individuals in total including different expressions. Main conclusions are as follows: (1) Image resolution has little influence on the gender categorization performance, and there is no guarantee that higher resolution images can obtain better results. (2) Fusion is useful to improve the categorization performance in each single modality. (3) Good local texture descriptors can substantially improve the gender categorization performance, which is even better than that in fusion.