  Deep learning-based methods have been the key driving force behind much of
the recent success of facial expression recognition (FER) systems. However, the
need for large amounts of labelled data remains a challenge. Semi-supervised
learning offers a way to overcome this limitation, allowing models to learn
from a small amount of labelled data along with a large unlabelled dataset.
While semi-supervised learning has shown promise in FER, most current methods
from general computer vision literature have not been explored in the context
of FER. In this work, we present a comprehensive study on 11 of the most recent
semi-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. Our
evaluation includes five FER datasets plus one large face dataset for
unconstrained learning. Our results demonstrate that FixMatch consistently
achieves better performance on in-distribution unlabelled data, while
ReMixMatch stands out among all methods for out-of-distribution, unconstrained,
and scarce unlabelled data scenarios. Another significant observation is that
semi-supervised learning produces a reasonable improvement over supervised
learning, regardless of whether in-distribution, out-of-distribution, or
unconstrained data is utilized as the unlabelled set. We also conduct
sensitivity analyses on critical hyper-parameters for the two best methods of
each setting.
