  Recognizing human emotion/expressions automatically is quite an expected
ability for intelligent robotics, as it can promote better communication and
cooperation with humans. Current deep-learning-based algorithms may achieve
impressive performance in some lab-controlled environments, but they always
fail to recognize the expressions accurately for the uncontrolled in-the-wild
situation. Fortunately, facial action units (AU) describe subtle facial
behaviors, and they can help distinguish uncertain and ambiguous expressions.
In this work, we explore the correlations among the action units and facial
expressions, and devise an AU-Expression Knowledge Constrained Representation
Learning (AUE-CRL) framework to learn the AU representations without AU
annotations and adaptively use representations to facilitate facial expression
recognition. Specifically, it leverages AU-expression correlations to guide the
learning of the AU classifiers, and thus it can obtain AU representations
without incurring any AU annotations. Then, it introduces a knowledge-guided
attention mechanism that mines useful AU representations under the constraint
of AU-expression correlations. In this way, the framework can capture local
discriminative and complementary features to enhance facial representation for
facial expression recognition. We conduct experiments on the challenging
uncontrolled datasets to demonstrate the superiority of the proposed framework
over current state-of-the-art methods. Codes and trained models are available
at https://github.com/HCPLab-SYSU/AUE-CRL.
