Decoding Emotions: How Graph Transformer with Adaptive Graph Structure Learning Understands Micro-Expressions

Micro-expressions are brief and subtle facial movements. Unlike macro-expressions, they are difficult to control and can reflect true emotion. Therefore, they are valuable in criminal investigation, medical care and other applications. Micro-expression recognition refers to the emotion classification of micro-expression samples. Previous approaches frequently relied on image sequences as inputs and ignored the fact that micro-expressions are activated only in local areas, introducing irrelevant noise. Additionally, some methods solely employed traditional graph models without fully exploring the complex spatiotemporal relationships between different facial regions and frames. To address this issue, we propose the method with a Graph Transformer for micro-expression recognition to more effectively learn the interrelations between facial regions and frames, thereby obtaining more discriminative features. Specifically, we develop a novel Graph Transformer with a learnable adjacency matrix for spatiotemporal learning, which better learns long-range dependencies and adaptively integrates implicit information in the graph. We select appropriate facial landmarks and calculate the optical-flow-based feature to serve as input. Finally, experiments conducted on relevant datasets have demonstrated the effectiveness of our method.