Scrambling faces for privacy protection using background self-similarities
Andrea Melle, Jean‐Luc Dugelay · 2014
The pervasive adoption of video surveillance systems demands tools for protecting the privacy of the persons being monitored. Current solutions are either naïve or they lack of important characteristics, such as reversibility or visual quality preservation. In this paper, we propose a novel scrambling procedure for protecting privacy sensitive image regions, which encodes the sensitive data in a parametric form, exploiting the visual information in the remaining part of the image. The encoded data is encrypted with a secret key. Partial knowledge of encryption key gives a protected version of the original image at variable levels of scrambling, while the knowledge of the full key allows decryption to a quality level suitable for people identification. To evaluate the proposed approach, we apply our scrambling filter to the AT&T face recognition dataset and we measure the resulting quality with an objective metric.