Auditing Demographic Bias in Facial Landmark Detection for Fair Human-Robot Interaction

ArXi:2604.06961v1 Announce Type: new Fairness in human-robot interaction critically depends on the reliability of the perceptual models that enable robots to interpret human behavior. While graphic biases have been widely studied in high-level facial analysis tasks, their presence in facial landmark detection remains unexplored. In this paper, we conduct a systematic audit of graphic bias in this task, analyzing the age, gender and race biases. To this end we