  We propose an end-to-end architecture for facial expression recognition. Our
model learns an optimal tree topology for facial landmarks, whose traversal
generates a sequence from which we obtain an embedding to feed a sequential
learner. The proposed architecture incorporates two main streams, one focusing
on landmark positions to learn the structure of the face, while the other
focuses on patches around the landmarks to learn texture information. Each
stream is followed by an attention mechanism and the outputs are fed to a
two-stream fusion component to perform the final classification. We conduct
extensive experiments on two large-scale publicly available facial expression
datasets, AffectNet and FER2013, to evaluate the efficacy of our approach. Our
method outperforms other solutions in the area and sets new state-of-the-art
expression recognition rates on these datasets.
