  Face identity masking algorithms developed in recent years aim to protect the
privacy of people in video recordings. These algorithms are designed to
interfere with identification, while preserving information about facial
actions. An important challenge is to preserve subtle actions in the eye
region, while obscuring the salient identity cues from the eyes. We evaluated
the effectiveness of identity-masking algorithms based on Canny filters,
applied with and without eye enhancement, for interfering with identification
and preserving facial actions. In Experiments 1 and 2, we tested human
participants' ability to match the facial identity of a driver in a low
resolution video to a high resolution facial image. Results showed that both
masking methods impaired identification, and that eye enhancement did not alter
the effectiveness of the Canny filter mask. In Experiment 3, we tested action
preservation and found that neither method interfered significantly with driver
action perception. We conclude that relatively simple, filter-based masking
algorithms, which are suitable for application to low quality video, can be
used in privacy protection without compromising action perception.
