摘要： 微表情是一种转瞬即逝、不受主观意识支配的面部肌肉运动，能够揭示个体试图隐藏的真实情绪。然而，微表情识别任务面临持续时间短、运动强度低、局部特征细微、公开数据规模有限以及个体差异明显等诸多挑战，限制了传统方法的识别准确率与泛化能力。为此，该研究提出一种基于动态路由专家的单流细粒度微表情识别方法。受混合专家模型启发，该方法以动态路由专家替代Transformer中传统的多头自注意力层，通过稀疏激活策略动态筛选专家网络，并借助专家间的协作机制增强特征表示能力，从而在保持计算效率的同时，提升模型表征容量。此外，设计了一种多粒度非对称聚合模块，该模块结合方向感知卷积与通道注意力，能够有效解耦空间特征并在不同网络层次自适应调整特征粒度，从而更精准地捕捉微表情的细微定向运动与局部纹理变化。在SAMM、SMIC和CASME II三个公开数据集上的实验表明，所提方法性能显著优于主流方法。在复合数据集上，未加权平均召回率与未加权F1分数分别达到87.65%和87.21%。实验结果验证了该方法在捕捉微表情细微动态特征方面的有效性，为复杂场景下的情感识别提供了可靠的技术支持。

Abstract: Micro-expressions are fleeting, involuntary facial muscle movements that can reveal genuine emotions individuals attempt to conceal. However, micro-expression recognition faces numerous challenges, including short duration, low intensity, prominent local features, limited scale of public datasets, and significant individual differences, which constrain the recognition accuracy and generalization capability of traditional methods. To address these issues, this study proposes a single-stream fine-grained micro-expression recognition method based on dynamic routing experts. Inspired by the mixture-of-experts model, this method replaces the traditional multi-head self-attention layer in Transformers with a dynamic routing expert mechanism. It dynamically selects expert networks through a sparse activation strategy and leverages a collaboration mechanism among experts to enhance feature representation capability, thereby improving model representational capacity while maintaining computational efficiency. Additionally, a multi-grained asymmetric aggregation module is designed, which integrates orientation-aware convolution and channel attention to effectively decouple spatial features and adaptively adjust feature granularity at different network levels, enabling more precise capture of subtle directional movements and local texture variations in micro-expressions. Experiments conducted on three public datasets, SAMM, SMIC, and CASME II, demonstrate that the proposed method significantly outperforms mainstream approaches. On the composite dataset, the method achieves an unweighted average recall of 87.65% and an unweighted F1-score of 87.21%. The experimental results validate the effectiveness of this method in capturing subtle dynamic features of micro-expressions, providing reliable technical support for emotion recognition in complex scenarios.