Exploring the magnitude of human sexual dimorphism in 3D face gender classification

Abstract

Human faces demonstrate clear Sexual Dimorphism (SD) for recognizing the gender. Different faces, even of the same gender, convey different magnitude of sexual dimorphism. However, in gender classification, gender has been interpreted discretely as either male or female. The exact magnitude of the sexual dimorphism in each gender is ignored. In this paper, we propose to evaluate the SD magnitude, using the ratio of votes from the Random Forest algorithm performed on 3D geometric features related to the face morphology. Then, faces are separated into a Low-SD group and a High-SD group. In the Intra-group experiments, when the training is performed with scans of similar SD magnitude than the testing scan, the classification accuracy improves. In Inter-group experiments, the scans with low magnitude of SD demonstrate higher gender discrimination power than the ones with high SD magnitude. With a decision-level fusion method, our method achieves 97.46% gender classification rate on the 466 earliest 3D scans of FRGCv2 (mainly neutral), and 97.18% on the whole FRGCv2 dataset (with expressions).