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. Recently,
formal methods for the de-identification of images
have been proposed which would benefit from multi-factor
coding to separate identity and non-identity related factors.
However, existing multi-factor models require complete labels
during training which are often not available in practice.
In this paper we propose a new multi-factor framework
which unifies linear, bilinear, and quadratic models. We describe
a new fitting algorithm which jointly estimates all
model parameters and show that it outperforms the standard
alternating algorithm. We furthermore describe how
to avoid overfitting the model and how to train the model
in a semi-supervised manner. In experiments on a large
expression-variant face database we show that data coded
using our multi-factor model leads to improved data utility
while providing the same privacy protection.
Ralph Gross, Latanya Sweeney, Fernando de la Torre, and Simon Baker. Semi-Supervised Learning of Multi-Factor Models for Face De-Identification. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, AK, June 2008.(PDF).