Improving Facial Expression Recognition with a Focal Transformer and Partial Feature Masking Augmentation

With the advancement of deep learning (DL) and computer vision (CV) technologies, significant progress has been made in facial expression identification FER for real-world applications. However, FER still faces challenges such as occlusion and head pose variations, which make it difficult for FER models to maintain stability and accuracy. In this study, we introduced a focal vision transformer (FViT) with partial feature masking (PFM) into FER. This method was found to efficiently simulate the challenges posed by occlusion and head pose variations by introducing PFM data augmentation. Parts of the image were randomly masked while preserving key facial expressions. The proposed FViT showed an accuracy of 89.08% on the real-world affective faces database, which includes scenarios with occlusion and head pose variations. PFM enhanced the model’s performance, too. The developed method effectively addresses the challenges of occlusion and head pose variations in FER.