Learning from Synthetic Data: Facial Expression Classification based on Ensemble of Multi-task Networks
This addresses facial expression recognition for interactive computing domains, but appears incremental as it builds on existing multi-task learning methods for a specific competition challenge.
The paper tackles facial expression recognition in-the-wild by proposing a multi-task learning approach with emotion and appearance branches that share face information, achieving a mean F1 score of 0.71 in a synthetic data learning challenge.
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.