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.

本文通过构建几何二流图网络、自学习机制和自适应动作单元损失函数，研究了人脸标志物对微表情识别的贡献。实验证明，本文提出的方法具有更高的效率和更低的计算成本，且人脸标志物对微表情识别有重要贡献，值得进一步研究高效的微表情分析。