Region Attention Enhanced Unsupervised Cross-Domain Facial Emotion Recognition

The visual emotion recognition from facial expressions easily suffers barrier problems of varying brightness, head pose change, various image scales when the recognition is performed in different domains. Therefore, it is required to erase such domain barriers. Considering that the human expresses their emotions always relying on the muscle motion near five sense organs of face, local features around them are typically crucial. In this paper, we propose a Region Attention eNhanced Domain Adaptation (RANDA) approach for unsupervised cross-domain facial expression recognition (FER). In RANDA, we design an unsupervised domain adaptation solution that adopts an iterative pseudo label assignment method to provide pseudo labels in the target domain, then employs adversarial learning to confuse feature representation of facial expressions in the source and target domains. Furthermore, a facial landmark guided fine-grained region attention learning module is designed to enhance significant emotion features and simultaneously weaken domain discrepancy. The proposed RANDA is adopted for cross-domain emotion recognition, and extensive evaluations are performed on multiple datasets, i.e., CK+, MMI, SFEW, RAF-DB, AffectNet. Results indicate that the RANDA outperforms the state-of-the-art approaches. It provides an effective solution for the cross-domain FER.