A wide range of technological advances have helped
to make extensive image and video acquisition close to effortless.
As a consequence many applications which capture image
data of people for either immediate inspection or storage and
subsequent sharing have become possible. Along with these
improved recording capabilities, however, come concerns about
the privacy of people visible in the scene. While algorithms
have been proposed to de-identify images, currently available
methods are still lacking. In this paper we propose a general
framework for the de-identification of images which subsumes
a number of previously introduced approaches. Unlike the adhoc
methods currently used in the field our algorithms aim at
providing privacy guarantees. In experiments on illuminationand
expression-variant face datasets we show that the proposed
algorithms achieve the desired privacy protection while minimally
distorting the data.
Ralph Gross and Latanya Sweeney. Towards Real-World Face De-Identification. IEEE Conference on Biometrics (BTAS), Washington, DC, September 2007. (PDF).