Adaptive Temporal Motion Guided Graph Convolution Network for Micro-expression Recognition

• URL: http://arxiv.org/abs/2406.08997v1
• Date: Thu, 13 Jun 2024 10:57:24 GMT
• Title: Adaptive Temporal Motion Guided Graph Convolution Network for Micro-expression Recognition
• Authors: Fengyuan Zhang, Zhaopei Huang, Xinjie Zhang, Qin Jin,
• Abstract summary: 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.
• Score: 48.21696443824074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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)$^3$.

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