  Facial expressions are combinations of basic components called Action Units
(AU). Recognizing AUs is key for developing general facial expression analysis.
In recent years, most efforts in automatic AU recognition have been dedicated
to learning combinations of local features and to exploiting correlations
between Action Units. In this paper, we propose a deep neural architecture that
tackles both problems by combining learned local and global features in its
initial stages and replicating a message passing algorithm between classes
similar to a graphical model inference approach in later stages. We show that
by training the model end-to-end with increased supervision we improve
state-of-the-art by 5.3% and 8.2% performance on BP4D and DISFA datasets,
respectively.
