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Face De-identification

  • Chapter
Protecting Privacy in Video Surveillance

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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. In most of these applications, knowledge of the identity of people in the image is not required. This makes the case for image de-identification, the removal of identifying information from images, prior to sharing of the data. Privacy protection methods are well established for field-structured data; however, work on images is still limited. In this chapter, we review previously proposed naïve and formal face de-identification methods. We then describe a novel framework for the de-identification of face images using multi-factor models which unify linear, bilinear, and quadratic data models. We show in experiments on a large expression-variant face database that the new algorithm is able to protect privacy while preserving data utility. The new model extends directly to image sequences, which we demonstrate on examples from a medical face database.

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Author information

Authors and Affiliations

  1. Data Privacy Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

    Ralph Gross

  2. Data Privacy Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

    Latanya Sweeney

  3. Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA

    Jeffrey Cohn

  4. Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Fernando de la Torre

  5. Microsoft Research, Microsoft Corporation, Redmond, WA 98052, USA

    Simon Baker

Authors
  1. Ralph Gross
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  2. Latanya Sweeney
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  3. Jeffrey Cohn
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  4. Fernando de la Torre
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  5. Simon Baker
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Corresponding author

Correspondence to Ralph Gross .

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Editors and Affiliations

  1. Google Research, New York, USA

    Andrew Senior

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Gross, R., Sweeney, L., Cohn, J., de la Torre, F., Baker, S. (2009). Face De-identification. In: Senior, A. (eds) Protecting Privacy in Video Surveillance. Springer, London. https://doi.org/10.1007/978-1-84882-301-3_8

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  • DOI: https://doi.org/10.1007/978-1-84882-301-3_8

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-300-6

  • Online ISBN: 978-1-84882-301-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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