  Facial action units (AUs) detection is fundamental to facial expression
analysis. As AU occurs only in a small area of the face, region-based learning
has been widely recognized useful for AU detection. Most region-based studies
focus on a small region where the AU occurs. Focusing on a specific region
helps eliminate the influence of identity, but bringing a risk for losing
information. It is challenging to find balance. In this study, I propose a
simple strategy. I divide the face into three broad regions, upper, middle, and
lower region, and group AUs based on where it occurs. I propose a new
end-to-end deep learning framework named three regions based attention network
(TRA-Net). After extracting the global feature, TRA-Net uses a hard attention
module to extract three feature maps, each of which contains only a specific
region. Each region-specific feature map is fed to an independent branch. For
each branch, three continuous soft attention modules are used to extract
higher-level features for final AU detection. In the DISFA dataset, this model
achieves the highest F1 scores for the detection of AU1, AU2, and AU4, and
produces the highest accuracy in comparison with the state-of-the-art methods.
