  Understanding human affective behaviour, especially in the dynamics of
real-world settings, requires Facial Expression Recognition (FER) models to
continuously adapt to individual differences in user expression, contextual
attributions, and the environment. Current (deep) Machine Learning (ML)-based
FER approaches pre-trained in isolation on benchmark datasets fail to capture
the nuances of real-world interactions where data is available only
incrementally, acquired by the agent or robot during interactions. New learning
comes at the cost of previous knowledge, resulting in catastrophic forgetting.
Lifelong or Continual Learning (CL), on the other hand, enables adaptability in
agents by being sensitive to changing data distributions, integrating new
information without interfering with previously learnt knowledge. Positing CL
as an effective learning paradigm for FER, this work presents the Continual
Facial Expression Recognition (ConFER) benchmark that evaluates popular CL
techniques on FER tasks. It presents a comparative analysis of several CL-based
approaches on popular FER datasets such as CK+, RAF-DB, and AffectNet and
present strategies for a successful implementation of ConFER for Affective
Computing (AC) research. CL techniques, under different learning settings, are
shown to achieve state-of-the-art (SOTA) performance across several datasets,
thus motivating a discussion on the benefits of applying CL principles towards
human behaviour understanding, particularly from facial expressions, as well
the challenges entailed.
