Facial expressions help individuals convey their emotions. In recent years, thanks to the development of computer vision technology, facial expression recognition (FER) has become a research hotspot and made remarkable progress. However, human faces in real-world environments are affected by various unfavorable factors, such as facial occlusion and head pose changes, which are seldom encountered in controlled laboratory settings. These factors often lead to a reduction in expression recognition accuracy. Inspired by the recent success of transformers in many computer vision tasks, we propose a model called the fine-tuned channel–spatial attention transformer (FT-CSAT) to improve the accuracy of recognition of FER in the wild. FT-CSAT consists of two crucial components: channel–spatial attention module and fine-tuning module. In the channel–spatial attention module, the feature map is input into the channel attention module and the spatial attention module sequentially. The final output feature map will effectively incorporate both channel information and spatial information. Consequently, the network becomes adept at focusing on relevant and meaningful features associated with facial expressions. To further improve the model’s performance while controlling the number of excessive parameters, we employ a fine-tuning method. Extensive experimental results demonstrate that our FT-CSAT outperforms the state-of-the-art methods on two benchmark datasets: RAF-DB and FERPlus. The achieved recognition accuracy is 88.61% and 89.26%, respectively. Furthermore, to evaluate the robustness of FT-CSAT in the case of facial occlusion and head pose changes, we take tests on Occlusion-RAF-DB and Pose-RAF-DB data sets, and the results also show that the superior recognition performance of the proposed method under such conditions.

In recent years, significant progress has been made in facial expression recognition methods. However, tasks related to facial expression recognition in real environments still require further research. This paper proposes a tri-cross-attention transformer with a multi-feature fusion network (TriCAFFNet) to improve facial expression recognition performance under challenging conditions. By combining LBP (Local Binary Pattern) features, HOG (Histogram of Oriented Gradients) features, landmark features, and CNN (convolutional neural network) features from facial images, the model is provided with a rich input to improve its ability to discern subtle differences between images. Additionally, tri-cross-attention blocks are designed to facilitate information exchange between different features, enabling mutual guidance among different features to capture salient attention. Extensive experiments on several widely used datasets show that our TriCAFFNet achieves the SOTA performance on RAF-DB with 92.17%, AffectNet (7 cls) with 67.40%, and AffectNet (8 cls) with 63.49%, respectively.