An Evaluation of Forensic Facial Recognition
Justin Norman Shruti Agarwal Hany Farid
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.