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%。

法医面部识别评估

An Evaluation of Forensic Facial Recognition

Face recognition (FR) is one of the most extensively investigated problems in
computer vision. Significant progress in FR has been made due to the recent
introduction of the larger scale FR challenges, particularly with constrained
social media web images, e.g. high-resolution photos of celebrity faces taken
by professional photo-journalists. However, the more challenging FR in
unconstrained and low-resolution surveillance images remains largely
under-studied. To facilitate more studies on developing FR models that are
effective and robust for low-resolution surveillance facial images, we
introduce a new Surveillance Face Recognition Challenge, which we call the
QMUL-SurvFace benchmark. This new benchmark is the largest and more importantly
the only true surveillance FR benchmark to our best knowledge, where
low-resolution images are not synthesised by artificial down-sampling of native
high-resolution images. This challenge contains 463,507 face images of 15,573
distinct identities captured in real-world uncooperative surveillance scenes
over wide space and time. As a consequence, it presents an extremely
challenging FR benchmark. We benchmark the FR performance on this challenge
using five representative deep learning face recognition models, in comparison
to existing benchmarks. We show that the current state of the arts are still
far from being satisfactory to tackle the under-investigated surveillance FR
problem in practical forensic scenarios. Face recognition is generally more
difficult in an open-set setting which is typical for surveillance scenarios,
owing to a large number of non-target people (distractors) appearing open
spaced scenes. This is evidently so that on the new Surveillance FR Challenge,
the top-performing CentreFace deep learning FR model on the MegaFace benchmark
can now only achieve 13.2% success rate (at Rank-20) at a 10% false alarm rate.

介绍了 QMUL-SurvFace benchmark，提供了低分辨率监控图像的面部识别挑战，利用五个具有代表性的深度学习面部识别模型进行了评估，结果表明目前的技术仍然远不能满足实际的刑事犯罪调查需求。