Uncertain Label Correction via Auxiliary Action Unit Graphs for Facial Expression Recognition
This addresses label noise in facial expression recognition datasets, which can mislead training, but it is incremental as it builds on existing graph-based and regularization techniques.
The paper tackles uncertain labels in facial expression recognition datasets by correcting them using auxiliary action unit graphs, achieving 89.31% accuracy on RAF-DB and 61.57% on AffectNet, outperforming baseline and state-of-the-art methods.
High-quality annotated images are significant to deep facial expression recognition (FER) methods. However, uncertain labels, mostly existing in large-scale public datasets, often mislead the training process. In this paper, we achieve uncertain label correction of facial expressions using auxiliary action unit (AU) graphs, called ULC-AG. Specifically, a weighted regularization module is introduced to highlight valid samples and suppress category imbalance in every batch. Based on the latent dependency between emotions and AUs, an auxiliary branch using graph convolutional layers is added to extract the semantic information from graph topologies. Finally, a re-labeling strategy corrects the ambiguous annotations by comparing their feature similarities with semantic templates. Experiments show that our ULC-AG achieves 89.31% and 61.57% accuracy on RAF-DB and AffectNet datasets, respectively, outperforming the baseline and state-of-the-art methods.