  Over the past few years, deep learning methods have shown remarkable results
in many face-related tasks including automatic facial expression recognition
(FER) in-the-wild. Meanwhile, numerous models describing the human emotional
states have been proposed by the psychology community. However, we have no
clear evidence as to which representation is more appropriate and the majority
of FER systems use either the categorical or the dimensional model of affect.
Inspired by recent work in multi-label classification, this paper proposes a
novel multi-task learning (MTL) framework that exploits the dependencies
between these two models using a Graph Convolutional Network (GCN) to recognize
facial expressions in-the-wild. Specifically, a shared feature representation
is learned for both discrete and continuous recognition in a MTL setting.
Moreover, the facial expression classifiers and the valence-arousal regressors
are learned through a GCN that explicitly captures the dependencies between
them. To evaluate the performance of our method under real-world conditions we
perform extensive experiments on the AffectNet and Aff-Wild2 datasets. The
results of our experiments show that our method is capable of improving the
performance across different datasets and backbone architectures. Finally, we
also surpass the previous state-of-the-art methods on the categorical model of
AffectNet.
