Ralph Gross, Latanya Sweeney, Fernando de la Torre, and Simon Baker
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.
|Published in||Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition|
|Publisher||IEEE Computer Society|
Copyright © 2007 IEEE. Reprinted from IEEE Computer Society. This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to email@example.com. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.