Gender Classification via Graph Convolutional Networks on 3D Facial Models

The automatic classification of human gender and other demographic attributes such as age and ethnicity is gaining significant attention. These attributes provide rich information with applications in personalization, behavior analysis, consumer research, digital forensics, security, human-computer interaction, and mobile applications. In the literature, the face is a commonly used feature for gender classification. In this paper, we follow this attitude but referring to 3D face data that offer advantages in terms of capturing spatial information and reducing sensitivity to ethnicity and acquisition conditions. In particular, we address gender classification using RGB-D data, which is structured as graphs and processed using a Graph Convolutional Neural Network (GCNN). Experiments conducted on the BP4D+ dataset demonstrate the effectiveness of this approach.