  Facial expression in-the-wild is essential for various interactive computing
domains. Especially, "Learning from Synthetic Data" (LSD) is an important topic
in the facial expression recognition task. In this paper, we propose a
multi-task learning-based facial expression recognition approach which consists
of emotion and appearance learning branches that can share all face
information, and present preliminary results for the LSD challenge introduced
in the 4th affective behavior analysis in-the-wild (ABAW) competition. Our
method achieved the mean F1 score of 0.71.
