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De-identification Project |
For most data usage scenarios however, 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. The implicit goal of these methods is to protect
privacy and preserve data utility, for example the
ability to recognize gender or facial expressions from
de-identified images. While privacy protection methods
are well established for field-structured data, work on
images is still limited.
The appearance of a face is influenced by a number
of factors including (but not limited to) identity, pose,
facial expression, illumination, and gender. Algorithms
have been developed to factorize facial appearance into
these underlying components using linear and bilinear
as well as tensor models. In this paper we describe a
novel framework for the de-identification of facial images.
We use Active Appearance Models (AAMs) for
initial parameterization, before we apply a multi-factor
model which unifies linear, bilinear, and quadratic models
to factorize the face parameters into identity and
non-identity components. The resulting representation
is then de-identified according to one of multiple privacy
protection models.
We evaluate the proposed face de-identification system
on a large-scale still image face database, the CMU
Multi-PIE database. We measure face recognition performance
on de-identified images as well as data utility
(quantified as accuracy of facial expression recognition)
using a support vector machine classifier trained
on the original data. In comparison to naive blur deidentification
as well as to the previously proposed formal
k-Same algorithm our method preserves data utility
much better while providing equivalent privacy protection.
Our framework directly extends from single images
to image sequences. We demonstrate this by deidentifying
videos of subjects displaying pain expressions.
Citation:
Abstract
Recent advances in both camera technology as well as
supporting computing hardware have made it significantly
easier to acquire, transmit, process and store
large amounts of image data. As a consequence a
number of image databases, specifically face databases,
have been collected, often with the expressed goal of
sharing the data with others. Due to concerns about
the privacy of the individuals visible in the scene, data
dissemination is particularly difficult for medical face
databases depicting patients.
Ralph Gross,
Latanya Sweeney,
Jeffrey Cohn, Fernando de la Torre, and Simon Baker.
Model-Based De-Identification of Facial Images.
Proceedings of the 2008 American Medical Informatics Association Annual Symposium, 2008.
(PDF).
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