Adaptive Graph Attention Network with Temporal Fusion for Micro-Expressions Recognition

Automatic micro-expression recognition (MER) has essential applications in the psychological field. Graph-based models, due to their advantages in analyzing regionalized faces, have become a powerful method for MER. However, how to construct a graph from ME videos remains to be studied. To solve this problem, we design an adaptive graph attention network with temporal fusion to model the dynamic relationships between facial regions of interest (ROIs). Specifically, we first propose adaptive graph attention to establish learnable spatial graphs from ME videos. Then, we adopt an optical-flow-based feature as the suitable input for the graph network. In addition, an implicit semantic data augmentation algorithm is employed and improved as a data-driven weighted loss for better performance. Extensive experiments on SMIC-HS, CASME II and SAMM datasets have demonstrated the effectiveness of the proposed method, and it achieves to be the first graph-based model where UF1 and UAR both exceed 0.90 for 3-classes MER on CASME II. Code will be available at https://github.com/MEA-LAB-421/ICME2023-Recognition.