CFFN: Cascaded Feature Fusion Network for Facial Expression Recognition

Facial Expression Recognition (FER) has vital applications in computer vision, multimedia, and human behavior analysis. The real-world environment presents a significant challenge for FER due to variations such as face pose changes and occlusions. Many existing methods rely on features extracted from the end of the backbone model, which often capture high-level semantic information, to make final predictions. In this paper, we proposed a novel Cascaded Feature Fusion Network (CFFN) for FER in the wild, where different levels of semantic features are learned to enrich the information flow to the downstream modules. To effectively use the facial cues, a multi-branch cascaded network structure was proposed, where a novel semantic feature fusion block (SFFB) was developed to integrate semantic features from neighboring branches and a novel multi-branch fusion block (MBFB) was proposed to obtain the final semantic features by integrating all branches. Experimental results on two benchmark datasets show that the proposed method utilizes image features more effectively, achieves state-of-the-art FER performance, and generalizes well across different demographic groups.