In this investigation we identify relationships between human and automated face recognition systems with respect to
compression. Further, we identify the most influential scene parameters on the performance of each recognition system.
The work includes testing of the systems with compressed Closed-Circuit Television (CCTV) footage, consisting of
quantified scene (footage) parameters. Parameters describe the content of scenes concerning camera to subject distance,
facial angle, scene brightness, and spatio-temporal busyness. These parameters have been previously shown to affect the
human visibility of useful facial information, but not much work has been carried out to assess the influence they have
on automated recognition systems. In this investigation, the methodology previously employed in the human
investigation is adopted, to assess performance of three different automated systems: Principal Component Analysis,
Linear Discriminant Analysis, and Kernel Fisher Analysis. Results show that the automated systems are more tolerant to
compression than humans. In automated systems, mixed brightness scenes were the most affected and low brightness
scenes were the least affected by compression. In contrast for humans, low brightness scenes were the most affected and
medium brightness scenes the least affected. Findings have the potential to broaden the methods used for testing imaging
systems for security applications.