  Facial Action Coding System is an approach for modeling the complexity of
human emotional expression. Automatic action unit (AU) detection is a crucial
research area in human-computer interaction. This paper describes our
submission to the third Affective Behavior Analysis in-the-wild (ABAW)
competition 2022. We proposed a method for detecting facial action units in the
video. At the first stage, a lightweight CNN-based feature extractor is
employed to extract the feature map from each video frame. Then, an attention
module is applied to refine the attention map. The attention encoded vector is
derived using a weighted sum of the feature map and the attention scores later.
Finally, the sigmoid function is used at the output layer to make the
prediction suitable for multi-label AUs detection. We achieved a macro F1 score
of 0.48 on the ABAW challenge validation set compared to 0.39 from the baseline
model.
