  Recognition of expressions of emotions and affect from facial images is a
well-studied research problem in the fields of affective computing and computer
vision with a large number of datasets available containing facial images and
corresponding expression labels. However, virtually none of these datasets have
been acquired with consideration of fair distribution across the human
population. Therefore, in this work, we undertake a systematic investigation of
bias and fairness in facial expression recognition by comparing three different
approaches, namely a baseline, an attribute-aware and a disentangled approach,
on two well-known datasets, RAF-DB and CelebA. Our results indicate that: (i)
data augmentation improves the accuracy of the baseline model, but this alone
is unable to mitigate the bias effect; (ii) both the attribute-aware and the
disentangled approaches fortified with data augmentation perform better than
the baseline approach in terms of accuracy and fairness; (iii) the disentangled
approach is the best for mitigating demographic bias; and (iv) the bias
mitigation strategies are more suitable in the existence of uneven attribute
distribution or imbalanced number of subgroup data.
