Keywords: Video surveillance, privacy, de-identification, privacy-preserving data mining,
With the emergence of new applications centered around the sharing of
image data, questions concerning the protection of the privacy of people visible in
the scene arise. In most of these applications knowledge of the identity of people in
the image is not required. This makes the case for image de-identification, the removal
of identifying information from images, prior to sharing of the data. Privacy
protection methods are well established for field-structured data, however, work on
images is still limited. In this chapter we review previously proposed naČ§ve and formal
face de-identification methods. We then describe a novel framework for the deidentification
of face images using multi-factor models which unify linear, bilinear,
and quadratic data models. We show in experiments on a large expression-variant
face database that the new algorithm is able to protect privacy while preserving data
utility. The new model extends directly to image sequences, which we demonstrate
on examples from a medical face database.
Ralph Gross, Latanya Sweeney, Jeffrey Cohn, Fernando de la Torre, and Simon Baker. In: Protecting Privacy in Video Surveillance, A. Senior, editor. Springer, 2009 Preserving Privacy by De-identifying Facial Images. (PDF).
Keywords: Video surveillance, privacy, de-identification, privacy-preserving data mining, k-anonymity, microaggregation