Abstract: Noise is inevitably present in datasets due to labeling subjectivity, image blurring, and other factors, making expression recognition more challenging. Existing facial expression recognition methods typically address noisy labels by partially overfitting to them. In this paper, a novel Noise-Resistant Dual Constraint Network (NDC-Net) is proposed to automatically suppress noisy samples. NDC-Net primarily consists of two constraint mechanisms: Class Activation mapping attention Consistency (CAC) and Channel and Spatial feature Consistency (CSC). CAC is used to make the model focus on locally important feature information and reduces the overfitting to noisy labels, while CSC is used to ensure that the model emphasizes task-relevant information from both channels and spatial dimensions during feature extraction, ignoring irrelevant information, and reducing reliance on noisy labels. Additionally, to enhance the performance of NDC-Net, input samples are augmented with strategies such as rotation and scaling. NDC-Net achieves recognition performances of 86.57%, 88.22%, and 59.78% under 30% label noise for RAF-DB, FERPlus, and AffectNet datasets, respectively. These results significantly outperform the state-of-the-art noisy labeling methods, such as EAC, NCCTFER. Moreover, NDC-Net also shows strong generalisation capability on general classification datasets such as CIFAR100 and Tiny-ImageNet.

Key words: noisy label learning, facial expression recognition, deep learning, supervised learning, attention mechanisms