SLNL: Soft Label Regularization For Semi-Supervised Facial Expression Recognition With Negative Label Learning

Semi-supervised learning (SSL) methods have been widely employed in facial expression recognition (FER) to eliminate the substantial cost of acquiring well-labeled samples. These methods typically involve assigning pseudo labels to unlabeled samples beyond a certain confidence threshold for calculating cross-entropy loss. However, they overlook the correctness issue with pseudo labels, potentially resulting in network over-fitting. In this paper, we introduce an SLNL model that incorporates a Soft Label Regularization (SLR) module and a Negative Label Learning (NLL) module, preventing network from over-fitting while enhancing the efficiency of utilizing unlabeled data. For each unlabeled sample whose pseudo-label confidence surpasses a threshold, SLR concurrently uses the pseudo label and the previously recorded soft label for supervised learning. Additionally, NLL dynamically explores negative labels by calculating top-k accuracy, further removing irrelevant information. The SLNL model achieves state-of-the-art performance across several widely used datasets, notably surpassing the fully-supervised baseline on AffectNet with fewer labeled data.