Learning Consistent Global-Local Representation for Cross-Domain Facial Expression Recognition

Domain shift is one of the knotty problems that seriously restricts the accuracy of cross-domain facial expression recognition. Most existing works mainly focus on learning domain-invariant features by global feature adaption, and little works are conducted using the local features which are more transferable across different domains. In this paper, a consistent global-local feature and semantic learning framework is proposed which can learn domain-invariant global and local feature representation, and generate pseudo labels to facilitate cross-domain facial expression recognition. Specifically, the proposed method first simultaneously learns the domain-invariant global and local features via separately adversarial global and local learning. Once those features are acquired, a global and local semantic consistency is introduced to help generate pseudo labels for unlabeled data of the target dataset. By performing such strategy, more efficiency pseudo labels with high accuracy are produced due to the information diversity in global-local features and do without the image transformation. We conduct extensive experiments and analyses on several public datasets to demonstrate the effectiveness of the proposed model.