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.

翻译：“从合成数据(LSD)学习”是面部表达式识别任务的一个重要话题。在本文中,我们建议采取多任务学习的面部表达式识别方法,包括情感和外观学习分支,可以分享所有面部信息,并介绍第四轮视觉行为分析(ABAW)竞赛中引入的LSD挑战的初步结果。我们的方法达到了0.71的F1平均分。