An Enhanced Adaptive Confidence Margin for Semi-Supervised Facial Expression Recognition

Semi-supervised learning (SSL) provides a practical framework for leveraging massive unlabeled samples, especially when labels are expensive for facial expression recognition (FER). Typical SSL methods like FixMatch select unlabeled samples with confidence scores above a fixed threshold for training. However, these methods face two primary limitations: failing to consider the varying confidence across facial expression categories and failing to utilize unlabeled facial expression samples efficiently. To address these challenges, we propose an Enhanced Adaptive Confidence Margin (EACM), consisting of dynamic thresholds for different categories, to fully learn unlabeled samples. Specifically, we employ the predictions on labeled samples at each training iteration to learn an EACM. It then partitions unlabeled samples into two subsets: (1) subset I, including samples whose confidence scores are no less than the margin; (2) subset II, including samples whose confidence scores are less than the margin. For samples in subset I, we constrain their predictions on strongly-augmented versions to match the pseudo-labels derived from the predictions on weakly-augmented versions. Meanwhile, we introduce a feature-level contrastive objective to enhance the similarity between two weakly-augmented features of a sample in subset II. We extensively evaluate EACM on image-based and video-based facial expression datasets, showing that our method achieves superior performance, significantly surpassing fully-supervised baselines in a semi-supervised manner. Additionally, our EACM is promising to leverage cross-dataset unlabeled samples for practical training to boost fully-supervised performance.