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

Towards Fair Affective Robotics: Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition