DocumentCode :
2395519
Title :
Semi-supervised learning of multi-factor models for face de-identification
Author :
Gross, Ralph ; Sweeney, Latanya ; De la Torre, Fernando ; Baker, Simon
Author_Institution :
Data Privacy Lab., Carnegie Mellon Univ., Pittsburgh, PA
fYear :
2008
fDate :
23-28 June 2008
Firstpage :
1
Lastpage :
8
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 :
face recognition; image coding; learning (artificial intelligence); security of data; data coding; expression-variant face database; face deidentification; fitting algorithm; image data sharing; multifactor coding; multifactor models; privacy protection; semisupervised learning; Data privacy; Databases; Image coding; Layout; Parameter estimation; Protection; Robots; Runtime; Semisupervised learning; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on
Conference_Location :
Anchorage, AK
ISSN :
1063-6919
Print_ISBN :
978-1-4244-2242-5
Electronic_ISBN :
1063-6919
Type :
conf
DOI :
10.1109/CVPR.2008.4587369
Filename :
4587369
Link To Document :
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