Action Units Recognition by Pairwise Deep Architecture

Junya Saito, Ryosuke Kawamura, Akiyoshi Uchida, Sachihiro Youoku, Yuushi Toyoda, Takahisa Yamamoto, Xiaoyu Mi, Kentaro Murase

Abstract: 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.