An Ensemble Approach for Facial Expression Analysis in Video

• URL: http://arxiv.org/abs/2203.12891v1
• Date: Thu, 24 Mar 2022 07:25:23 GMT
• Title: An Ensemble Approach for Facial Expression Analysis in Video
• Authors: Hong-Hai Nguyen and Van-Thong Huynh and Soo-Hyung Kim
• Abstract summary: This paper introduces the Affective Behavior Analysis in-the-wild (ABAW3) 2022 challenge. The paper focuses on solving the problem of the. valence-arousal estimation and action unit detection.
• Score: 5.363490780925308
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human emotions recognization contributes to the development of human-computer interaction. The machines understanding human emotions in the real world will significantly contribute to life in the future. This paper will introduce the Affective Behavior Analysis in-the-wild (ABAW3) 2022 challenge. The paper focuses on solving the problem of the valence-arousal estimation and action unit detection. For valence-arousal estimation, we conducted two stages: creating new features from multimodel and temporal learning to predict valence-arousal. First, we make new features; the Gated Recurrent Unit (GRU) and Transformer are combined using a Regular Networks (RegNet) feature, which is extracted from the image. The next step is the GRU combined with Local Attention to predict valence-arousal. The Concordance Correlation Coefficient (CCC) was used to evaluate the model.

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