  As affective robots become integral in human life, these agents must be able
to fairly evaluate human affective expressions without discriminating against
specific demographic groups. Identifying bias in Machine Learning (ML) systems
as a critical problem, different approaches have been proposed to mitigate such
biases in the models both at data and algorithmic levels. In this work, we
propose Continual Learning (CL) as an effective strategy to enhance fairness in
Facial Expression Recognition (FER) systems, guarding against biases arising
from imbalances in data distributions. We compare different state-of-the-art
bias mitigation approaches with CL-based strategies for fairness on expression
recognition and Action Unit (AU) detection tasks using popular benchmarks for
each; RAF-DB and BP4D. Our experiments show that CL-based methods, on average,
outperform popular bias mitigation techniques, strengthening the need for
further investigation into CL for the development of fairer FER algorithms.
