Recognizing micro-expressions (MEs) as subtle and transient forms of human emotional expressions is critical for accurately judging human feelings. However, recognizing MEs is challenging due to their transient and low-intensity characteristics. This study develops a lightweight shallow dual-group symmetric attention network (SDGSA) to address the limitations of existing methods in capturing the subtle features of MEs. This network takes the optical flow features as inputs, extracting ME features through a shallow network and performing finer feature segmentation in the channel dimension through a dual-group strategy. The goal is to focus on different types of facial information without disrupting facial symmetry. Moreover, this study implements a spatial symmetry attention module, focusing on extracting facial symmetry features to emphasize further the symmetric information of the left and right sides of the face. Additionally, we introduce the channel blending technique to optimize the information fusion between different channel features. Extensive experiments on SMIC, CASME II, SAMM, and 3DB-combined mainstream ME datasets demonstrate that the proposed SDGSA method outperforms the metrics of current state-of-the-art methods. As shown by ablation experimental results, the proposed dual-group symmetric attention module outperforms classical attention modules, such as the convolutional block attention module, squeeze-and-excitation, efficient channel attention, spatial group-wise enhancement, and multi-head self-attention. Importantly, SDGSA maintained excellent performance while having only 0.278 million parameters. The code and model are publicly available at https://github.com/YZY980123/SDGSA.

将微表情（ME）识别为人类情感表达的微妙而短暂的形式对于准确判断人类情感至关重要。然而，由于 ME 的瞬态和低强度特性，识别 ME 具有挑战性。本研究开发了一种轻量级浅层双组对称注意力网络（SDGSA），以解决现有方法在捕获 ME 微妙特征方面的局限性。该网络以光流特征为输入，通过浅层网络提取ME特征，并通过双组策略在通道维度上进行更精细的特征分割。目标是在不破坏面部对称性的情况下关注不同类型的面部信息。此外，本研究实现了空间对称注意力模块，重点提取面部对称特征，以进一步强调面部左右两侧的对称信息。此外，我们引入了通道混合技术来优化不同通道特征之间的信息融合。在 SMIC、CASME II、SAMM 和 3DB 组合的主流 ME 数据集上进行的大量实验表明，所提出的 SDGSA 方法优于当前最先进方法的指标。消融实验结果表明，所提出的双组对称注意模块优于经典注意模块，例如卷积块注意模块、挤压和激励、高效通道注意、空间分组增强和多头自学习注意力。重要的是，SDGSA 在仅有 27.8 万个参数的情况下保持了出色的性能。代码和模型可在 https://github.com/YZY980123/SDGSA 上公开获取。