  As Facial Expression Recognition (FER) systems become integrated into our
daily lives, these systems need to prioritise making fair decisions instead of
aiming at higher individual accuracy scores. Ranging from surveillance systems
to diagnosing mental and emotional health conditions of individuals, these
systems need to balance the accuracy vs fairness trade-off to make decisions
that do not unjustly discriminate against specific under-represented
demographic groups. Identifying bias as a critical problem in facial analysis
systems, different methods have been proposed that aim to mitigate bias both at
data and algorithmic levels. In this work, we propose the novel usage of
Continual Learning (CL), in particular, using Domain-Incremental Learning
(Domain-IL) settings, as a potent bias mitigation method to enhance the
fairness of FER systems while guarding against biases arising from skewed data
distributions. We compare different non-CL-based and CL-based methods for their
classification accuracy and fairness scores on expression recognition and
Action Unit (AU) detection tasks using two popular benchmarks, the RAF-DB and
BP4D datasets, respectively. Our experimental results show that CL-based
methods, on average, outperform other popular bias mitigation techniques on
both accuracy and fairness metrics.
