A Computational Other-Race-Effect Analysis for 3D Facial Expression Recognition

This paper investigates the other-race-effects in automatic 3D facial expression recognition, giving the computational analysis of the recognition performance obtained from two races, namely white and east Asian. The 3D face information is represented by local depth feature, and then a feature learning process is used to obtain race-sensitive features to simulate the other-race-effect. The learned features from own race and other race are then used to do facial expression recognition. The proposed analysis is conducted on BU-3DFE database, and the results show that the learned features from one race achieve better recognition performance on the own-race faces. It reveals that the other-race-effect are significant in facial expression recognition problem, which confirms the results of psychological experiment results.