Identity masking effectiveness and gesture recognition: Effects of eye enhancement in seeing through the mask
This work addresses privacy protection in video recordings for applications like surveillance or driver monitoring, but it is incremental as it builds on existing filter-based methods without introducing major innovations.
The study evaluated identity-masking algorithms using Canny filters, with and without eye enhancement, to protect privacy in videos by impairing identification while preserving facial actions like driver gestures. Results showed both methods impaired identification without affecting action perception, confirming their suitability for low-quality video privacy protection.
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.