用眼白区域评估人脸图像质量，实现跨肤色公平性
Eye Sclera for Fair Face Image Quality Assessment
- 以眼白区域替代传统皮肤区域进行图像质量评估
- 在不同肤色群体中均保持一致的评估准确率
- 适合需要公平性的面部识别系统部署
公正的运行系统对建立社会对人脸识别系统（FRS）的信任至关重要。FRS首先需采集图像并评估其质量，再用于注册或验证。因此，公平的人脸图像质量评估（FIQA）方案同样重要。本文研究将眼白区域作为质量评估区域，以实现公平的FIQA。眼白区域不受人种差异和肤色影响，可有效评估图像动态范围及过曝、欠曝问题。我们分析了三个与肤色相关的ISO/IEC人脸图像质量评估指标，并验证眼白区域作为替代评估区域的可行性。基于涵盖不同人种、肤色的面部数据集分析表明，仅使用眼白区域即可准确评估图像质量。通过误差-弃用特性（EDC）曲线分析，眼白区域因不依赖肤色，具备同等公平性，适合作为公平的FIQA方案。
原文摘要 · Abstract (English)
Fair operational systems are crucial in gaining and maintaining society's trust in face recognition systems (FRS). FRS start with capturing an image and assessing its quality before using it further for enrollment or verification. Fair Face Image Quality Assessment (FIQA) schemes therefore become equally important in the context of fair FRS. This work examines the sclera as a quality assessment region for obtaining a fair FIQA. The sclera region is agnostic to demographic variations and skin colour for assessing the quality of a face image. We analyze three skin tone related ISO/IEC face image quality assessment measures and assess the sclera region as an alternative area for assessing FIQ. Our analysis of the face dataset of individuals from different demographic groups representing different skin tones indicates sclera as an alternative to measure dynamic range, over- and under-exposure of face using sclera region alone. The sclera region being agnostic to skin tone, i.e., demographic factors, provides equal utility as a fair FIQA as shown by our Error-vs-Discard Characteristic (EDC) curve analysis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。