Investigating Bias and Fairness in Appearance-based Gaze Estimation
For researchers and developers of gaze estimation systems, this work highlights the need for robust, equitable models by establishing a fairness baseline and identifying open issues.
This paper presents the first comprehensive evaluation of fairness in appearance-based gaze estimation, revealing significant performance disparities across ethnicity and gender. Existing bias mitigation strategies show limited effectiveness in this domain.
While appearance-based gaze estimation has achieved significant improvements in accuracy and domain adaptation, the fairness of these systems across different demographic groups remains largely unexplored. To date, there is no comprehensive benchmark quantifying algorithmic bias in gaze estimation. This paper presents the first extensive evaluation of fairness in appearance-based gaze estimation, focusing on ethnicity and gender attributes. We establish a fairness baseline by analyzing state-of-the-art models using standard fairness metrics, revealing significant performance disparities. Furthermore, we evaluate the effectiveness of existing bias mitigation strategies when applied to the gaze domain and show that their fairness contributions are limited. We summarize key insights and open issues. Overall, our work calls for research into developing robust, equitable gaze estimators. To support future research and reproducibility, we publicly release our annotations, code, and trained models at: github.com/akgulburak/gaze-estimation-fairness