Face Expression Recognition on Uncertainty-Based Robust Sample Selection Strategy

In the task of Facial Expression Recognition (FER), data uncertainty has been a critical factor affecting performance, typically arising from the ambiguity of facial expressions, low-quality images, and the subjectivity of annotators. Tracking the training history reveals that misclassified samples often exhibit high confidence and excessive uncertainty in the early stages of training. To address this issue, we propose an uncertainty-based robust sample selection strategy, which combines confidence error with RandAugment to improve image diversity, effectively reducing overfitting caused by uncertain samples during deep learning model training. To validate the effectiveness of the proposed method, extensive experiments were conducted on FER public benchmarks. The accuracy obtained were 89.08% on RAF-DB, 63.12% on AffectNet, and 88.73% on FERPlus.