用开放人脸质量标准评估身份证图像质量，提升远程验证安全
Image Quality Assessment of Identity Cards Using Measures from Open Face Image Quality
- 引入OFIQ标准中的捕捉质量指标，结合角点检测与透视校正预处理
- 在四组身份证数据集上验证，部分指标可显著提升攻击检测准确率
- 适合身份认证系统开发者、安全验证研究人员参考
本文针对远程验证系统中身份证图像质量评估难题，将开放人脸图像质量（OFIQ）标准中的捕获相关质量指标应用于身份证图像。预处理流程包含角点检测、透视归一化和全面前景遮罩，确保质量度量计算的准确性和无偏性。通过分析这些指标在四个多样化身份证数据集上的表现，评估其与三种呈现攻击检测（PAD）算法性能的相关性。其中两组数据为真实原始图像，另两组为打印伪造证件。结果表明，基于部分OFIQ指标的质量评估可显著提升PAD性能。
原文摘要 · Abstract (English)
This paper addresses the challenge of assessing image quality in ID cards in remote verification systems by applying capture-related quality measures from the Open Face Image Quality (OFIQ) standard to ID card images. Our preprocessing pipeline includes corner detection, perspective normalization, and comprehensive foreground masking to ensure accurate and unbiased quality measure computation. We evaluate the effectiveness of these measures by analyzing their correlation with the performance of three presentation attack detection (PAD) algorithms across four diverse ID card datasets, where two datasets contain bona fide, i.e. pristine, images and two contain printed mock ID cards. Our results suggest that quality assessment based on some OFIQ measures can significantly improve PAD performance.
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