Compact selective transformer based on information entropy for facial expression recognition in the wild

Facial expression recognition (FER) in the wild is a challenging task due to pose variations, occlusions, etc. Many studies employ region-based methods to relieve the influence of occlusions and pose variations. However, these methods often neglect the global relationship between local regions. To address these problems, we introduce a compact selective transformer into ResNet-50 (R-CST) for in-thewild FER. First, we develop a compact transformer to capture the global relationship between local regions outputted by the intermediate of ResNet-50. Then, an information entropy-based selective module is added to the compact transformer to select discriminative information and drop the background and occlusions. Finally, we combine the intermediate features and the last convolutional features of R-CST for emotion classification. Experimental results on three in-the-wild FER datasets demonstrate that the proposed R-CST outperforms several state-of-the-art FER models. Codes are available at https://github.com/Gabrella/R-CST.