On bias and fairness in deep learning-based facial analysis

Facial analysis systems are used in a variety of scenarios such as law enforcement, military, and daily life, which impact important aspects of our lives. With the onset of the deep learning era, neural networks are being widely used for the development of facial analysis systems. However, existing systems have been shown to yield disparate performance across different demographic subgroups. This has led to unfair outcomes for certain members of society. With an aim to provide fair treatment in the face of diversity, it has become imperative to study the biased behavior of systems. It is crucial that these systems do not discriminate based on the gender, identity, skin tone, or ethnicity of individuals. In recent years, a section of the research community has started to focus on the fairness of such deep learning systems. In this work, we survey the research that has been done in the direction of analyzing fairness and the techniques used to mitigate bias. A taxonomy for the bias mitigation techniques is provided. We also discuss the databases proposed in the research community for studying bias and the relevant evaluation metrics. Lastly, we discuss the open challenges in the field of biased facial analysis.