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An Ensemble Approach for Facial Expression Analysis in Video

2022-03-24 07:25:23
Hong-Hai Nguyen, Van-Thong Huynh, Soo-Hyung Kim

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.

Abstract (translated)

URL

https://arxiv.org/abs/2203.12891

PDF

https://arxiv.org/pdf/2203.12891.pdf


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