  Facial expression is an essential factor in conveying human emotional states
and intentions. Although remarkable advancement has been made in facial
expression recognition (FER) task, challenges due to large variations of
expression patterns and unavoidable data uncertainties still remain. In this
paper, we propose mid-level representation enhancement (MRE) and graph embedded
uncertainty suppressing (GUS) addressing these issues. On one hand, MRE is
introduced to avoid expression representation learning being dominated by a
limited number of highly discriminative patterns. On the other hand, GUS is
introduced to suppress the feature ambiguity in the representation space. The
proposed method not only has stronger generalization capability to handle
different variations of expression patterns but also more robustness to capture
expression representations. Experimental evaluation on Aff-Wild2 have verified
the effectiveness of the proposed method.

Computer Vision and Pattern Recognition;Artificial Intelligence