  Facial Expression Recognition (FER) suffers from data uncertainties caused by
ambiguous facial images and annotators' subjectiveness, resulting in excursive
semantic and feature covariate shifting problem. Existing works usually correct
mislabeled data by estimating noise distribution, or guide network training
with knowledge learned from clean data, neglecting the associative relations of
expressions. In this work, we propose an Adaptive Graph-based Feature
Normalization (AGFN) method to protect FER models from data uncertainties by
normalizing feature distributions with the association of expressions.
Specifically, we propose a Poisson graph generator to adaptively construct
topological graphs for samples in each mini-batches via a sampling process, and
correspondingly design a coordinate descent strategy to optimize proposed
network. Our method outperforms state-of-the-art works with accuracies of
91.84% and 91.11% on the benchmark datasets FERPlus and RAF-DB, respectively,
and when the percentage of mislabeled data increases (e.g., to 20%), our
network surpasses existing works significantly by 3.38% and 4.52%.
