  Facial action unit (AU) recognition is essential to facial expression
analysis. Since there are highly positive or negative correlations between AUs,
some existing AU recognition works have focused on modeling AU relations.
However, previous relationship-based approaches typically embed predefined
rules into their models and ignore the impact of various AU relations in
different crowds. In this paper, we propose a novel Attention Based Relation
Network (ABRNet) for AU recognition, which can automatically capture AU
relations without unnecessary or even disturbing predefined rules. ABRNet uses
several relation learning layers to automatically capture different AU
relations. The learned AU relation features are then fed into a self-attention
fusion module, which aims to refine individual AU features with attention
weights to enhance the feature robustness. Furthermore, we propose an AU
relation dropout strategy and AU relation loss (AUR-Loss) to better model AU
relations, which can further improve AU recognition. Extensive experiments show
that our approach achieves state-of-the-art performance on the DISFA and DISFA+
datasets.
