检测人脸关键点时，年龄偏差影响显著，性别与种族偏差在控制视觉因素后消失。
Auditing Demographic Bias in Facial Landmark Detection for Fair Human-Robot Interaction
- 分离视觉干扰因素，系统评估年龄、性别、种族对关键点检测的影响。
- 年龄偏差明显：老年人定位误差更高，而性别和种族差异在控制后消失。
- 揭示低层视觉任务中公平性问题，适用于关注机器人感知公平性的研究者。
人机交互的公平性依赖于感知模型的可靠性。尽管高层面部分析中的族裔偏差已被广泛研究，但人脸关键点检测中的偏差仍缺乏探索。本文系统审计了该任务中的年龄、性别和种族偏差，提出一种受控的统计方法以分离种族效应与视觉混淆因素（如头姿和图像分辨率）。分析表明，视觉混淆因素（尤其是头姿与面部分辨率）对性能的影响远超种族属性。值得注意的是，在校正这些混淆因素后，性别与种族间的性能差异消失。然而，我们发现存在显著的年龄相关偏差：老年人的关键点定位误差更高。这表明，即使在底层视觉组件中也可能出现公平性问题，并会沿人机交互流程传播。我们认为，审计并纠正此类偏差是构建可信且公平的机器人感知系统的关键步骤。
原文摘要 · Abstract (English)
Fairness in human-robot interaction critically depends on the reliability of the perceptual models that enable robots to interpret human behavior. While demographic 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 demographic bias in this task, analyzing the age, gender, and race biases. To this end, we introduce a controlled statistical methodology to disentangle demographic effects from confounding visual factors. Our analysis demonstrates that visual confounders, particularly head pose and face resolution, heavily outweigh the impact of demographic attributes. Notably, after accounting for these confounders, performance disparities across gender and race vanish. However, we identify a statistically significant age-related bias, with higher localization errors for older individuals. This shows that fairness issues can emerge even in low-level vision components and can propagate through the HRI pipeline. We argue that auditing and correcting such biases is a necessary step toward trustworthy and equitable robot perception systems.
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