  Automatic Micro-Expression (ME) spotting in long videos is a crucial step in
ME analysis but also a challenging task due to the short duration and low
intensity of MEs. When solving this problem, previous works generally lack in
considering the structures of human faces and the correspondence between
expressions and relevant facial muscles. To address this issue for better
performance of ME spotting, this paper seeks to extract finer spatial features
by modeling the relationships between facial Regions of Interest (ROIs).
Specifically, we propose a graph convolutional-based network, called
Action-Unit-aWare Graph Convolutional Network (AUW-GCN). Furthermore, to inject
prior information and to cope with the problem of small datasets, AU-related
statistics are encoded into the network. Comprehensive experiments show that
our results outperform baseline methods consistently and achieve new SOTA
performance in two benchmark datasets,CAS(ME)^2 and SAMM-LV. Our code is
available at https://github.com/xjtupanda/AUW-GCN.
