  Given the similarity between facial expression categories, the presence of
compound facial expressions, and the subjectivity of annotators, facial
expression recognition (FER) datasets often suffer from ambiguity and noisy
labels. Ambiguous expressions are challenging to differentiate from expressions
with noisy labels, which hurt the robustness of FER models. Furthermore, the
difficulty of recognition varies across different expression categories,
rendering a uniform approach unfair for all expressions. In this paper, we
introduce a novel approach called Adaptive Sample Mining (ASM) to dynamically
address ambiguity and noise within each expression category. First, the
Adaptive Threshold Learning module generates two thresholds, namely the clean
and noisy thresholds, for each category. These thresholds are based on the mean
class probabilities at each training epoch. Next, the Sample Mining module
partitions the dataset into three subsets: clean, ambiguity, and noise, by
comparing the sample confidence with the clean and noisy thresholds. Finally,
the Tri-Regularization module employs a mutual learning strategy for the
ambiguity subset to enhance discrimination ability, and an unsupervised
learning strategy for the noise subset to mitigate the impact of noisy labels.
Extensive experiments prove that our method can effectively mine both ambiguity
and noise, and outperform SOTA methods on both synthetic noisy and original
datasets. The supplement material is available at
https://github.com/zzzzzzyang/ASM.
