  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.
