Recent advances in machine learning and computer vision have led to reported
facial recognition accuracies surpassing human performance. We question if
these systems will translate to real-world forensic scenarios in which a
potentially low-resolution, low-quality, partially-occluded image is compared
against a standard facial database. We describe the construction of a
large-scale synthetic facial dataset along with a controlled facial forensic
lineup, the combination of which allows for a controlled evaluation of facial
recognition under a range of real-world conditions. Using this synthetic
dataset, and a popular dataset of real faces, we evaluate the accuracy of two
popular neural-based recognition systems. We find that previously reported face
recognition accuracies of more than 95% drop to as low as 65% in this more
challenging forensic scenario.

最近机器学习和计算机视觉的进展使得人脸识别的准确率超越了人类表现。然而，我们质疑这些系统在真实的法庭鉴定情景中是否能够有效应用，因为在这种情景下，图像往往是低分辨率、低质量且被部分遮挡。本研究构建了一个大规模的合成人脸数据集，并创建了一个控制的人脸法庭鉴定场景，通过这两者的结合，可以在一系列真实环境条件下对人脸识别进行控制性评估。通过使用这个合成数据集和一个包含真实人脸的流行数据集，我们评估了两个常见的基于神经网络的人脸识别系统的准确性。我们发现，在这种更具挑战性的法庭鉴定情景中，先前报道的超过 95% 的人脸识别准确率下降到了 65%。