Learning from Synthetic Data: Facial Expression Classification based on Ensemble of Multi-task Networks

• URL: http://arxiv.org/abs/2207.10025v2
• Date: Thu, 21 Jul 2022 07:59:17 GMT
• Title: Learning from Synthetic Data: Facial Expression Classification based on Ensemble of Multi-task Networks
• Authors: Jae-Yeop Jeong, Yeong-Gi Hong, JiYeon Oh, Sumin Hong, Jin-Woo Jeong, Yuchul Jung
• Abstract summary: "Learning from Synthetic Data" (LSD) is an important topic in the facial expression recognition task. We propose a multi-task learning-based facial expression recognition approach. Our method achieved the mean F1 score of 0.71.
• Score: 3.736069053271373
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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