Advances in camera and computing equipment hardware
in recent years have made it increasingly simple to capture
and store extensive amounts of video data. This, among
other things, creates ample opportunities for the sharing of
video sequences. In order to protect the privacy of subjects
visible in the scene, automated methods to de-identify the
images, particularly the face region, are necessary. So far
the majority of privacy protection schemes currently used in
practice rely on ad-hoc methods such as pixelation or blurring
of the face. In this paper we show in extensive experiments
that pixelation and blurring offers very poor privacy
protection while significantly distorting the data. We then
introduce a novel framework for de-identifying facial images.
Our algorithm combines a model-based face image
parameterization with a formal privacy protection model.
In experiments on two large-scale data sets we demonstrate
privacy protection and preservation of data utility.
Ralph Gross and Latanya Sweeney, Fernando de la Torre, and Simon Baker. Model-Based Face De-Identification. 22nd IEEE International Conference on Data Engineering, (ICDE). Atlanta, GA, April 2006. (PDF).