  The Affective Behavior Analysis in-the-wild (ABAW) 2022 Competition gives
Affective Computing a large promotion. In this paper, we present our method of
AU challenge in this Competition. We use improved IResnet100 as backbone. Then
we train AU dataset in Aff-Wild2 on three pertained models pretrained by our
private au and expression dataset, and Glint360K respectively. Finally, we
ensemble the results of our models. We achieved F1 score (macro) 0.731 on AU
validation set.
