  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.
