Facial Action Units (AUs) represent a set of facial muscular activities and various combinations of AUs can represent a wide range of emotions. AU recognition is often used in many applications, including marketing, healthcare, education, and so forth. Although a lot of studies have developed various methods to improve recognition accuracy, it still remains a major challenge 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 the Pseudo-Intensities of each AU and then convert them into predicted intensities. This year, we introduced a new technique to last year's framework to further reduce AU recognition errors due to temporary face occlusion such as hands on face or large face orientation. We obtained a score of 0.65 in the validation data set for this year's competition.
@article{arxiv.2107.03143,
title = {Action Units Recognition Using Improved Pairwise Deep Architecture},
author = {Junya Saito and Xiaoyu Mi and Akiyoshi Uchida and Sachihiro Youoku and Takahisa Yamamoto and Kentaro Murase and Osafumi Nakayama},
journal= {arXiv preprint arXiv:2107.03143},
year = {2021}
}