通过调节光照平衡不同种族女性人脸识别差异，显著缩小评分差距。
Lights, Camera, Matching: The Role of Image Illumination in Fair Face Recognition
- 用肤色区域亮度中位数或分布来均衡光照
- 亮度分布均衡使准确率差距减少57.6%
- 改善了跨群体相似度评分，适合公平性研究者参考
面部亮度是影响不同族裔群体间人脸识别准确率差异的关键图像质量因素。本文旨在降低白人与非裔美国女性配对图像间相似度评分分布的d'差距。通过三种实验，将面部皮肤区域的亮度分别定义为像素值中位数或分布。仅基于亮度中位数均衡时，d'最大下降46.8%；基于亮度分布均衡时，最大下降57.6%。在所有情况下，各分布的相似度评分均提升，白人女性平均分最高提升5.9%，非裔女性最高提升3.7%。
原文摘要 · Abstract (English)
Facial brightness is a key image quality factor impacting face recognition accuracy differentials across demographic groups. In this work, we aim to decrease the accuracy gap between the similarity score distributions for Caucasian and African American female mated image pairs, as measured by d' between distributions. To balance brightness across demographic groups, we conduct three experiments, interpreting brightness in the face skin region either as median pixel value or as the distribution of pixel values. Balancing based on median brightness alone yields up to a 46.8% decrease in d', while balancing based on brightness distribution yields up to a 57.6% decrease. In all three cases, the similarity scores of the individual distributions improve, with mean scores maximally improving 5.9% for Caucasian females and 3.7% for African American females.
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