研究人脸图像质量评估在不同人群中的偏差，发现多数指标无明显偏见。
Demographic Variability in Face Image Quality Measures
- 测试国际标准中所有面部图像质量评估方法在年龄、性别、肤色上的表现
- 多数指标在各群体间表现一致，仅两项在肤色上差异显著
- 适合关注生物识别公平性的研究人员和系统开发者
人脸图像质量评估（FIQA）算法正被集成到在线身份管理应用中。用户上传人脸图像后，系统会自动进行质量评估以确保符合标准。由于生物识别系统可能存在社会影响，关于其种族偏见的担忧日益增加。因此，评估FIQA算法在不同人口统计学特征下的表现至关重要，以便制定缓解措施。本文研究了ISO/IEC 29794-5国际标准中所有面部图像质量评估方法在年龄、性别和皮肤色调三个维度上的表现。结果显示，大多数评估指标在各群体间表现稳定，未显示出明显偏向。仅有两个质量度量在皮肤色调维度上存在显著差异。
原文摘要 · Abstract (English)
Face image quality assessment (FIQA) algorithms are being integrated into online identity management applications. These applications allow users to upload a face image as part of their document issuance process, where the image is then run through a quality assessment process to make sure it meets the quality and compliance requirements. Concerns about demographic bias have been raised about biometric systems, given the societal implications this may cause. It is therefore important that demographic variability in FIQA algorithms is assessed such that mitigation measures can be created. In this work, we study the demographic variability of all face image quality measures included in the ISO/IEC 29794-5 international standard across three demographic variables: age, gender, and skin tone. The results are rather promising and show no clear bias toward any specific demographic group for most measures. Only two quality measures are found to have considerable variations in their outcomes for different groups on the skin tone variable.
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