Semi-Supervised Learning of Multi-Factor Models for Face De-Identification

Ralph Gross, Latanya Sweeney, Fernando De la Torre Frade, and Simon Baker
IEEE Conference on Computer Vision and Pattern Recognition, July, 2008.


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.

Keywords
multilinear models, face de-identification, semi-supervised learning

Notes
Note: the associated project is component analysis for data analysis and face group

Text Reference
Ralph Gross, Latanya Sweeney, Fernando De la Torre Frade, and Simon Baker, "Semi-Supervised Learning of Multi-Factor Models for Face De-Identification," IEEE Conference on Computer Vision and Pattern Recognition, July, 2008.

BibTeX Reference
@inproceedings{Gross_2008_6040,
   author = "Ralph Gross and Latanya Sweeney and Fernando {De la Torre Frade} and Simon Baker",
   title = "Semi-Supervised Learning of Multi-Factor Models for Face De-Identification",
   booktitle = "IEEE Conference on Computer Vision and Pattern Recognition",
   month = "July",
   year = "2008",
   Notes = "the associated project is component analysis for data analysis and face group"
}