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This paper describes an approach to the facial action unit (AU) detection. Inthis work, we present our submission to the Field Affective Behavior Analysis(ABAW) 2021 competition. The proposed method uses the pre-trained JAA model asthe feature extractor, and extracts global features, face alignment featuresand AU local features on the basis of multi-scale features. We take the AUlocal features as the input of the graph convolution to further consider thecorrelation between AU, and finally use the fused features to classify AU. Thedetected accuracy was evaluated by 0.5*accuracy + 0.5*F1. Our model achieves0.674 on the challenging Aff-Wild2 database.

Action Unit Detection with Joint Adaptive Attention and Graph Relation