Semi-Supervised Learning of Multi-Factor Models for Face De-Identification
People
- Ralph Gross
- Latanya Sweeney
- Fernando de la Torre
- Simon Baker
Abstract
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.
Citation
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Ralph Gross, Latanya Sweeney, Fernando de la Torre and S. Baker,
"Semi-Supervised Learning of Multi-Factor Models for Face De-Identification", IEEE Conference on Computer Vision and Pattern Recognition, June, 2008. [PDF] [Bibtex] |
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Ralph Gross, Latanya Sweeney, Jeffrey Cohn, Fernando de la Torre and Simon Bake,
"Face De-Identification", Protecting Privacy in Video Surveillance. (Ed.) Springer. [PDF] [Bibtex] |
Acknowledgements and Funding
This research is supported by: The National Institute of Justice, Fast Capture Initiative, under award number 2005-IJCX-K046.
Copyright notice
| Human Sensning Lab |