Face De-identification
Protecting Privacy in Video Surveillance(2009)
摘要
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 naïve and formal face de-identification methods. We then
describe a novel framework for the de-identification 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.
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