On 3d Face Attributes Analysis Using Deep Learning: A Preliminary Case Study on Gender and Ethnicity Recognition

Human faces provide us with not only identity, but also de-mographic attributes like gender and ethnicity. Recognizing such attributes from 2D face images has been rapidly developed due to deep learning (DL). However, it is still unknown about the effectiveness of DL in facial attributes analysis using 3D data. This paper systematically investigates the performance of DL-based 3D face gender and ethnicity recognition from three aspects: data representation, data augmentation, and comparison to the state-of-the-art. Using two typical deep networks in the literature, five representations including point clouds, depth images, normal maps, HHA (Horizontal disparity, Height over ground, and Angle between local surface normal and gravity direction) and DAE (Depth, Azimuth and Elevation angles of surface normal) maps are compared on two benchmark databases, FRGC v2 and BU3D-FE. Data augmentation by synthesizing multiview 3D faces is proven effective in cross-database evaluation, and the proposed DAE-based deep model effectively advances the state-of-the-art.