  Automatically understanding emotions from visual data is a fundamental task
for human behaviour understanding. While models devised for Facial Expression
Recognition (FER) have demonstrated excellent performances on many datasets,
they often suffer from severe performance degradation when trained and tested
on different datasets due to domain shift. In addition, as face images are
considered highly sensitive data, the accessibility to large-scale datasets for
model training is often denied. In this work, we tackle the above-mentioned
problems by proposing the first Source-Free Unsupervised Domain Adaptation
(SFUDA) method for FER. Our method exploits self-supervised pretraining to
learn good feature representations from the target data and proposes a novel
and robust cluster-level pseudo-labelling strategy that accounts for in-cluster
statistics. We validate the effectiveness of our method in four adaptation
setups, proving that it consistently outperforms existing SFUDA methods when
applied to FER, and is on par with methods addressing FER in the UDA setting.
