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                            Library & Multimedia
 Semi-supervised learning of multi-factor models for face de-identification
NCJ Number
              305649

Author(s)
          Ralph Gross;
                        Latanya Sweeney;
                        Fernando de la Torre;
                        Simon Baker
Date Published

2008
Length
              8 pages
Annotation
              This paper proposes a new multi-factor framework which unifies linear, bilinear, and quadratic models. The authors describe a new fitting algorithm which jointly estimates all model parameters and show that it outperforms the standard alternating algorithm.
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. 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 experiments on illumination-and expression-variant face datasets the authors show that the proposed algorithms achieve the desired privacy protection while minimally distorting the data. (Publisher abstract provided)
      
  
          Additional Details
Grant Number(s)
        2005-IJ-CX-K046


  
  
        
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        NIJ-Sponsored


  
  
        
  Award
        HandShot ID: A Fast 3-D Imaging System for Capturing Fingerprints, Palm Prints and Hand Geometry
    
  
  
Date Published: January 1, 2008
    
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      PatentsRelated Topics
        
                Face recognition technology
        Equipment and technology
        Face de-identification
        Privacy and security



  
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