  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.
