Adaptive Temporal Motion Guided Graph Convolution Network for Micro-expression Recognition
<|reference_start|>Adaptive Temporal Motion Guided Graph Convolution Network for Micro-expression Recognition: 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)$^3$.<|reference_end|>
@article{zhang2024adaptive,
    title={Adaptive Temporal Motion Guided Graph Convolution Network for
  Micro-expression Recognition},
    author={Fengyuan Zhang, Zhaopei Huang, Xinjie Zhang, Qin Jin},
    journal={arXiv preprint arXiv:2406.08997},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08997},
    primaryClass={cs.CV}
}