An ICA-Based Other-Race Effect Elimination for Facial Expression Recognition

Other-race effect affects the performance of multi-race facial expression recognition significantly. Though this phenomenon has been noticed by psychologists and computer vision researchers for decades, few work has been done to eliminate this influence caused by other-race effect. This work proposes an ICA-based other-race effect elimination method for 3D facial expression recognition. Firstly, the local depth features are extracted from 3D face point clouds, and then independent component analysis is used to project the features into a subspace in which the feature components are mutually independent. Second, a mutual information based feature selection method is adopted to determine race-sensitive features. Finally, the features after race-sensitive information elimination are utilized to conduct facial expression recognition. The proposed method is evaluated on BU-3DFE database, and the results reveal that the proposed method is effective to other-race effect elimination and could improve the multi-race facial expression recognition performance.