Local-Global Cross-Fusion Transformer Network for Facial Expression Recognition

Facial Expression Recognition (FER) has received increasing attention in the computer vision community. For FER, there are two challenging issues among the facial images: large inter-class similarity and small intra-class discrepancy. To address these challenges and obtain a better performance, we propose a Local-Global Cross-Fusion Transformer network in this paper. Specifically, the method seeks to obtain a more discriminative facial representation by sufficiently considering the local features of multiple local regions of the face and global face features. In order to extract the critical local area features of the face, a local feature decomposition module based on facial landmarks is designed. In addition, a local-global cross-fusion Transformer is designed to enhance the synergistic correlation between local features and global features using the cross-attention mechanism, which can maximize the focus on key regions while considering the connection information among local regions. Extensive experiments conducted on three mainstream expression recognition datasets, RAF-DB, FERPlus, and AffectNet, show that the method outperforms many existing expression recognition methods and can significantly improve the accuracy of expression recognition.