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Deep learning-based methods have been the key driving force behind much ofthe recent success of facial expression recognition (FER) systems. However, theneed for large amounts of labelled data remains a challenge. Semi-supervisedlearning offers a way to overcome this limitation, allowing models to learnfrom a small amount of labelled data along with a large unlabelled dataset.While semi-supervised learning has shown promise in FER, most current methodsfrom general computer vision literature have not been explored in the contextof FER. In this work, we present a comprehensive study on 11 of the most recentsemi-supervised methods, in the context of FER, namely Pi-model, Pseudo-label,Mean Teacher, VAT, UDA, MixMatch, ReMixMatch, FlexMatch, CoMatch, and CCSSL.Our investigation covers semi-supervised learning from in-distribution,out-of-distribution, unconstrained, and very small unlabelled data. Ourevaluation includes five FER datasets plus one large face dataset forunconstrained learning. Our results demonstrate that FixMatch consistentlyachieves better performance on in-distribution unlabelled data, whileReMixMatch stands out among all methods for out-of-distribution, unconstrained,and scarce unlabelled data scenarios. Another significant observation is thatsemi-supervised learning produces a reasonable improvement over supervisedlearning, regardless of whether in-distribution, out-of-distribution, orunconstrained data is utilized as the unlabelled set. We also conductsensitivity analyses on critical hyper-parameters for the two best methods ofeach setting.

Exploring the Boundaries of Semi-Supervised Facial Expression  Recognition: Learning from In-Distribution, Out-of-Distribution, and  Unconstrained Data