Facial expression spotting is a significant but challenging task in facial
expression analysis. The accuracy of expression spotting is affected not only
by irrelevant facial movements but also by the difficulty of perceiving subtle
motions in micro-expressions. In this paper, we propose a Multi-Scale
Spatio-Temporal Graph Convolutional Network (SpoT-GCN) for facial expression
spotting. To extract more robust motion features, we track both short- and
long-term motion of facial muscles in compact sliding windows whose window
length adapts to the temporal receptive field of the network. This strategy,
termed the receptive field adaptive sliding window strategy, effectively
magnifies the motion features while alleviating the problem of severe head
movement. The subtle motion features are then converted to a facial graph
representation, whose spatio-temporal graph patterns are learned by a graph
convolutional network. This network learns both local and global features from
multiple scales of facial graph structures using our proposed facial local
graph pooling (FLGP). Furthermore, we introduce supervised contrastive learning
to enhance the discriminative capability of our model for difficult-to-classify
frames. The experimental results on the SAMM-LV and CAS(ME)^2 datasets
demonstrate that our method achieves state-of-the-art performance, particularly
in micro-expression spotting. Ablation studies further verify the effectiveness
of our proposed modules.

本文提出了一种用于面部表情识别的多尺度时空图卷积网络 (SpoT-GCN)，通过接受域自适应滑窗策略提取更稳健的动态特征，并通过面部图形表示将微妙的动态特征转换为学习到的时空图模式，并使用提出的面部局部图池化 (FLGP) 从多个尺度的面部图结构中学习局部和全局特征，此外，引入了有监督对比学习来增强模型对难以分类的帧的区分能力。在 SAMM-LV 和 CAS (ME)^2 数据集上的实验结果表明，我们的方法在微表情识别方面达到了最先进的性能，消融研究进一步验证了我们提出的模块的有效性。