Combining Fixed-Weight ArcFace Loss and Vision Transformer for Facial Expression Recognition.

In recent years, deep learning has demonstrated remarkable capability in the broad domain of feature learning. Although the facial expression recognition task has been extensively studied, it still faces many challenges such as significant variations within the same category of expressions. ArcFace loss is a widely adopted function designed to enhance inter-class separability and improve recognition performance. However, it does not explicitly constrain the angular distribution between class centers. Therefore, this study introduces a weight-constrained ArcFace loss and integrates it into the Vision Transformer (ViT) framework. This approach not only alleviates implicit biases induced by imbalanced data distributions but also significantly reduces computational overhead by stabilizing weight optimization. In the experiments, the authors evaluated the proposed approach against standard ArcFace loss, classical loss functions, and various network structures on the RAF-DB and FER2013 datasets. Comprehensive experimental results demonstrate that the proposed approach not only improves recognition accuracy but also achieves higher computational efficiency.