首次系统评估外观眼神估计中的公平性问题，发现不同人群表现差异大。
Investigating Bias and Fairness in Appearance-based Gaze Estimation
- 基于肤色与性别分析主流模型的公平性表现
- 揭示主流方法在不同群体间误差差异显著
- 适合关注算法公平性的计算机视觉研究者
尽管基于外观的眼神估计在准确率和域适应方面取得了显著进展，但其在不同人口群体间的公平性仍缺乏深入研究。目前尚无全面的基准来量化眼神估计中的算法偏差。本文首次对基于外观的眼神估计公平性进行了广泛评估，重点关注种族与性别属性。通过使用标准公平性指标分析当前最先进的模型，建立了公平性基线，揭示了明显的性能差异。此外，我们评估了现有偏差缓解策略在眼神估计领域的有效性，结果表明其公平性提升有限。文章总结了关键洞察与未解问题，呼吁开发鲁棒且公平的眼神估计器。为支持未来研究与可复现性，我们公开发布标注数据、代码及训练模型：github.com/akgulburak/gaze-estimation-fairness。
原文摘要 · Abstract (English)
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
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