Adaptive Temporal Motion Guided Graph Convolution Network for Micro-expression Recognition
This work addresses micro-expression recognition for applications in fields like business negotiation and psychotherapy, representing an incremental improvement over existing methods.
The paper tackles the challenge of accurately recognizing micro-expressions by proposing the ATM-GCN framework, which captures temporal dependencies and motion features, achieving superior performance with state-of-the-art results on datasets like Composite and CAS(ME)^3.
Micro-expressions serve as essential cues for understanding individuals' genuine emotional states. Recognizing micro-expressions attracts increasing research attention due to its various applications in fields such as business negotiation and psychotherapy. However, the intricate and transient nature of micro-expressions poses a significant challenge to their accurate recognition. Most existing works either neglect temporal dependencies or suffer from redundancy issues in clip-level recognition. In this work, we propose a novel framework for micro-expression recognition, named the Adaptive Temporal Motion Guided Graph Convolution Network (ATM-GCN). Our framework excels at capturing temporal dependencies between frames across the entire clip, thereby enhancing micro-expression recognition at the clip level. Specifically, the integration of Adaptive Temporal Motion layers empowers our method to aggregate global and local motion features inherent in micro-expressions. Experimental results demonstrate that ATM-GCN not only surpasses existing state-of-the-art methods, particularly on the Composite dataset, but also achieves superior performance on the latest micro-expression dataset CAS(ME)$^3$.