Performance variation of morphed face image detection algorithms across different datasets
Performance variation of morphed face image detection algorithms across different datasets
复制标题
不同数据集上变形人脸图像检测算法的性能差异
DOI：
10.1109/iwbf.2018.8401562
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
C. Busch
中科院分区：
文献类型：
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作者：
U. Scherhag;C. Rathgeb;C. Busch
在过去的几年里，不同的研究人员已经表明了人脸识别系统对基于变形人脸图像的攻击的脆弱性。最近，已经提出了第一变形检测子系统来自动检测这种欺诈。虽然一些算法已被报道，以揭示实际的检测性能的个人数据集的系统分析，建议的检测器在不同的数据库之间的鲁棒性仍然难以捉摸。在这项工作中，我们评估了不同形态检测算法在2，745个真实和14，337个自动生成的变形人脸图像的不相交数据集上的性能。在一个通用的评估框架内，提出了一个系统的鲁棒性估计方案，以确定可靠的检测算法。最后，已被确定为最有前途的算法的鲁棒性在另一个不相交的数据集上进行了验证。因此，本文代表了第一次尝试对一个全面的跨数据库的性能评估和变形人脸图像检测算法的鲁棒性的系统评估。
In past years, different researchers have shown the vulnerability of face recognition systems to attacks based on morphed face images. More recently, first morph detection subsystems have been proposed to automatically detect this kind of fraud. While some algorithms have been reported to reveal practical detection performance on individual datasets a systematic analysis of proposed detectors with respect to their robustness across different databases has remained elusive. In this work, we evaluate the performance of different morph detection algorithms across disjoint datasets of 2,745 bona fide and 14,337 automatically generated morphed face images. Within a generic evaluation framework a systematic robustness estimation scheme is proposed to identify reliable detection algorithms. Finally, the robustness of algorithms which have been determined as most promising is verified on another disjoint dataset. Hence, this paper represents the first attempt towards a comprehensive cross-database performance evaluation and a systematic evaluation of the robustness of morphed face image detection algorithms.