Micro-expression recognition (MER) is valuable because micro-expressions (MEs) can reveal genuine emotions. Most works take image sequences as input and cannot effectively explore ME information because subtle ME-related motions are easily submerged in unrelated information. Instead, the facial landmark is a low-dimensional and compact modality, which achieves lower computational cost and potentially concentrates on ME-related movement features. However, the discriminability of facial landmarks for MER is unclear. Thus, this paper explores the contribution of facial landmarks and proposes a novel framework to efficiently recognize MEs. Firstly, a geometric two-stream graph network is constructed to aggregate the low-order and high-order geometric movement information from facial landmarks to obtain discriminative ME representation. Secondly, a self-learning fashion is introduced to automatically model the dynamic relationship between nodes even long-distance nodes. Furthermore, an adaptive action unit loss is proposed to reasonably build the strong correlation between landmarks, facial action units and MEs. Notably, this work provides a novel idea with much higher efficiency to promote MER, only utilizing graph-based geometric features. The experimental results demonstrate that the proposed method achieves competitive performance with a significantly reduced computational cost. Furthermore, facial landmarks significantly contribute to MER and are worth further study for high-efficient ME analysis.

翻译：微表情识别因能揭示真实情感而具有重要价值。现有方法大多以图像序列为输入，但微表情细微的运动信息易被无关信息淹没，难以有效挖掘。面部关键点作为低维紧凑模态，不仅计算成本较低，而且能聚焦于微表情相关运动特征。然而，面部关键点在微表情识别中的判别性尚不明确。为此，本文探索面部关键点的贡献，提出一种高效识别微表情的新框架。首先，构建几何双流图网络，聚合面部关键点中低阶和高阶几何运动信息，获取判别性微表情表示；其次，引入自学习机制自动建模节点间（甚至远距离节点）的动态关系；进一步提出自适应动作单元损失，合理构建关键点、面部动作单元与微表情之间的强关联。值得关注的是，本研究仅利用基于图的几何特征，为推进微表情识别提供了效率更高的新思路。实验结果表明，所提方法在显著降低计算成本的同时实现了具有竞争力的性能。此外，面部关键点对微表情识别具有重要贡献，值得在高效率微表情分析中深入研究。