  In this paper, we propose a new automatic Action Units (AUs) recognition
method used in a competition, Affective Behavior Analysis in-the-wild (ABAW).
Our method tackles a problem of AUs label inconsistency among subjects by using
pairwise deep architecture. While the baseline score is 0.31, our method
achieved 0.67 in validation dataset of the competition.
