  Facial expression recognition plays an important role in human-computer
interaction. In this paper, we propose the Coarse-to-Fine Cascaded network with
Smooth Predicting (CFC-SP) to improve the performance of facial expression
recognition. CFC-SP contains two core components, namely Coarse-to-Fine
Cascaded networks (CFC) and Smooth Predicting (SP). For CFC, it first groups
several similar emotions to form a rough category, and then employs a network
to conduct a coarse but accurate classification. Later, an additional network
for these grouped emotions is further used to obtain fine-grained predictions.
For SP, it improves the recognition capability of the model by capturing both
universal and unique expression features. To be specific, the universal
features denote the general characteristic of facial emotions within a period
and the unique features denote the specific characteristic at this moment.
Experiments on Aff-Wild2 show the effectiveness of the proposed CFSP. We
achieved 3rd place in the Expression Classification Challenge of the 3rd
Competition on Affective Behavior Analysis in-the-wild. The code will be
released at https://github.com/BR-IDL/PaddleViT.
