Learning the Connectivity: Situational Graph Convolution Network for Facial Expression Recognition

Previous studies recognizing expressions with facial graph topology mostly use a fixed facial graph structure established by the physical dependencies among facial landmarks. However, the static graph structure inherently lacks flexibility in non-standardized scenarios. This paper proposes a dynamic-graph-based method for effective and robust facial expression recognition. To capture action-specific dependencies among facial components, we introduce a link inference structure, called the Situational Link Generation Module (SLGM). We further propose the Situational Graph Convolution Network (SGCN) to automatically detect and recognize facial expression in various conditions. Experimental evaluations on two lab-constrained datasets, CK+ and Oulu, along with an in-the-wild dataset, AFEW, show the superior performance of the proposed method. Additional experiments on occluded facial images further demonstrate the robustness of our strategy.