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Facial Action Units (AUs) represent a set of facial muscular activities andvarious combinations of AUs can represent a wide range of emotions. AUrecognition is often used in many applications, including marketing,healthcare, education, and so forth. Although a lot of studies have developedvarious methods to improve recognition accuracy, it still remains a majorchallenge for AU recognition. In the Affective Behavior Analysis in-the-wild(ABAW) 2020 competition, we proposed a new automatic Action Units (AUs)recognition method using a pairwise deep architecture to derive thePseudo-Intensities of each AU and then convert them into predicted intensities.This year, we introduced a new technique to last year's framework to furtherreduce AU recognition errors due to temporary face occlusion such as hands onface or large face orientation. We obtained a score of 0.65 in the validationdata set for this year's competition.

Action Units Recognition Using Improved Pairwise Deep Architecture