  Most of the existing deep neural nets on automatic facial expression
recognition focus on a set of predefined emotion classes, where the amount of
training data has the biggest impact on performance. However, in the standard
setting over-parameterised neural networks are not amenable for learning from
few samples as they can quickly over-fit. In addition, these approaches do not
have such a strong generalisation ability to identify a new category, where the
data of each category is too limited and significant variations exist in the
expression within the same semantic category. We embrace these challenges and
formulate the problem as a low-shot learning, where once the base classifier is
deployed, it must rapidly adapt to recognise novel classes using a few samples.
In this paper, we revisit and compare existing few-shot learning methods for
the low-shot facial expression recognition in terms of their generalisation
ability via episode-training. In particular, we extend our analysis on the
cross-domain generalisation, where training and test tasks are not drawn from
the same distribution. We demonstrate the efficacy of low-shot learning methods
through extensive experiments.
