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Automatic Micro-Expression (ME) spotting in long videos is a crucial step inME analysis but also a challenging task due to the short duration and lowintensity of MEs. When solving this problem, previous works generally lack inconsidering the structures of human faces and the correspondence betweenexpressions and relevant facial muscles. To address this issue for betterperformance of ME spotting, this paper seeks to extract finer spatial featuresby modeling the relationships between facial Regions of Interest (ROIs).Specifically, we propose a graph convolutional-based network, calledAction-Unit-aWare Graph Convolutional Network (AUW-GCN). Furthermore, to injectprior information and to cope with the problem of small datasets, AU-relatedstatistics are encoded into the network. Comprehensive experiments show thatour results outperform baseline methods consistently and achieve new SOTAperformance in two benchmark datasets,CAS(ME)^2 and SAMM-LV. Our code isavailable at https://github.com/xjtupanda/AUW-GCN.

AU-aware graph convolutional network for Macro- and Micro-expression spotting