  Deep models for facial expression recognition achieve high performance by
training on large-scale labeled data. However, publicly available datasets
contain uncertain facial expressions caused by ambiguous annotations or
confusing emotions, which could severely decline the robustness. Previous
studies usually follow the bias elimination method in general tasks without
considering the uncertainty problem from the perspective of different
corresponding sources. In this paper, we propose a novel method of multi-task
assisted correction in addressing uncertain facial expression recognition
called MTAC. Specifically, a confidence estimation block and a weighted
regularization module are applied to highlight solid samples and suppress
uncertain samples in every batch. In addition, two auxiliary tasks, i.e.,
action unit detection and valence-arousal measurement, are introduced to learn
semantic distributions from a data-driven AU graph and mitigate category
imbalance based on latent dependencies between discrete and continuous
emotions, respectively. Moreover, a re-labeling strategy guided by
feature-level similarity constraint further generates new labels for identified
uncertain samples to promote model learning. The proposed method can flexibly
combine with existing frameworks in a fully-supervised or weakly-supervised
manner. Experiments on RAF-DB, AffectNet, and AffWild2 datasets demonstrate
that the MTAC obtains substantial improvements over baselines when facing
synthetic and real uncertainties and outperforms the state-of-the-art methods.
