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.

翻译：本文通过将开放式人脸图像质量（OFIQ）标准中与采集相关的质量度量应用于身份证图像，解决了远程验证系统中身份证图像质量评估的挑战。我们的预处理流程包括角点检测、透视归一化以及全面的前景掩膜处理，以确保质量度量计算的准确性和无偏性。我们通过分析这些度量与四种演示攻击检测（PAD）算法在四个不同身份证数据集上性能的相关性来评估其有效性，其中两个数据集包含真实图像（即原始图像），另外两个包含打印的模拟身份证。结果表明，基于某些OFIQ度量的质量评估能够显著提升PAD性能。