Skip to main content
SpringerLink
Log in

Face De-identification

  • Chapter
Protecting Privacy in Video Surveillance

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 16.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. A. Ashraf, S. Lucey, J.F. Cohn, T. Chen, Z. Ambadar, K. Prkachin, P. Solomon, and B.-J. Theobald. The painful face – pain expression recognition using active appearance models. In ICMI, 2007.

    Google Scholar 

  2. P. Baldi and K. Hornik. Neural networks and principal component analysis: Learning from examples without local minimia. Neural Networks, 2(1):53–58, 1989.

    Article  Google Scholar 

  3. A. Barucha, C. Atkeson, S. Stevens, D. Chen, H. Wactlar, B. Pollock, and M.A. Dew. Caremedia: Automated video and sensor analysis for geriatric care. In Annual Meeting of the American Association for Geriatric Psychiatry, 2006.

    Google Scholar 

  4. R. Beveridge, D. Bolme, B.A. Draper, and M. Teixeira. The CSU face identification evaluation system. Machine Vision and Applications, 16:128–138, 2005.

    Article  Google Scholar 

  5. C.M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995.

    Google Scholar 

  6. A. Björck. Numerical Methods for Least Squares Problems. SIAM: Society or Industrial and Applied Mathematics, 1996.

    MATH  Google Scholar 

  7. M. Boyle, C. Edwards, and S. Greenberg. The effects of filtered video on awareness and privacy. In ACM Conference on Computer Supported Cooperative Work, pages 1–10, Philadelphia, PA, December 2000.

    Chapter  Google Scholar 

  8. Y. Chang, R. Yan, D. Chen, and J. Yang. People identification with limited labels in privacy-protected video. In International Conference on Multimedia and Expo (ICME), 2006.

    Google Scholar 

  9. T. Cootes, G. Edwards, and C.J. Taylor. Active appearance models. IEEE Transaction on Pattern Analysis and Machine Intelligence, 23(6), 2001.

    Google Scholar 

  10. J. Crowley, J. Coutaz, and F. Berard. Things that see. Communications of the ACM, 43(3):54–64, 2000.

    Article  Google Scholar 

  11. F. Dufaux, M. Ouaret, Y. Abdeljaoued, A. Navarro, F. Vergnenegre, and T. Ebrahimi. Privacy enabling technology for video surveillance. In Proceedings of the SPIE 6250, 2006.

    Google Scholar 

  12. D.A. Fidaleo, H.-A. Nguyen, and M. Trivedi. The networked sensor tapestry (NeST): A privacy enhanced software architecture for interactive analysis of data in video-sensor networks. In Proceedings of the ACM 2nd International Workshop on Video Surveillance and Sensor Networks, 2004.

    Google Scholar 

  13. R. Gross. Face De-Identification using Multi-Factor Active Appearance Models. PhD thesis, Carnegie Mellon University, 2008.

    Google Scholar 

  14. R. Gross, E. Airoldi, B. Malin, and L. Sweeney. Integrating utility into face de-identification. In Workshop on Privacy Enhancing Technologies (PET), June 2005.

    Google Scholar 

  15. R. Gross, I. Matthews, J. Cohn, T. Kanade, and S. Baker. Multi-PIE. In 8th International Conference on Automatic Face and Gesture Recognition, 2008.

    Google Scholar 

  16. R. Gross, L. Sweeney, F. de la Torre, and S. Baker. Model-based face de-identification. In IEEE Workshop on Privacy Research in Vision, 2006.

    Google Scholar 

  17. R. Gross, L. Sweeney, T. de la Torre, and S. Baker. Semi-supervised learning of multi-factor models for face de-identification. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2008.

    Google Scholar 

  18. S. Hudson and I. Smith. Techniques for addressing fundamental privacy and disruption trade-offs in awareness support systems. In ACM Conference on Computer Supported Cooperative Work, pages 1–10, Boston, MA, November 1996.

    Google Scholar 

  19. I.T. Jolliffe. Principal Component Analysis. Springer, second edition, 2002.

    Google Scholar 

  20. T. Koshimizu, T. Toriyama, and N. Babaguchi. Factors on the sense of privacy in video surveillance. In Proceedings of the 3rd ACM Workshop on Continuous Archival and Retrival of Personal Experiences, pages 35–44, 2006.

    Google Scholar 

  21. I. Martinez-Ponte, X. Desurmont, J. Meessen, and J.-F. Delaigle. Robust human face hiding ensuring privacy. In Workshop on the Integration of Knowledge, Semantics and Digital Media Technology (WIAMIS), 2005.

    Google Scholar 

  22. I. Matthews and S. Baker. Active appearance models revisited. International Journal of Computer Vision, 60(2):135–164, 2004.

    Article  Google Scholar 

  23. C. Neustaedter and S. Greenberg. Balancing privacy and awareness in home media spaces. In Workshop on Ubicomp Communities: Privacy as Boundary Negotiation, 2003.

    Google Scholar 

  24. C. Neustaedter, S. Greenberg, and M. Boyle. Blur filtration fails to preserve privacy for home-based video conferencing. ACM Transactions on Computer Human Interactions (TOCHI), 2005.

    Google Scholar 

  25. E. Newton, L. Sweeney, and B. Malin. Preserving privacy by de-identifying facial images. IEEE Transactions on Knowledge and Data Engineering, 17(2):232–243, 2005.

    Article  Google Scholar 

  26. P.J. Phillips, H. Moon, S. Rizvi, and P.J Rauss. The FERET evaluation methodology for face-recognition algorithms. IEEE Transaction on Pattern Analysis and Machine Intelligence, 22(10):1090–1104, 2000.

    Article  Google Scholar 

  27. H. Schneiderman and T. Kanade. Object detection using the statistics of parts. International Journal of Computer Vision, 56(3):151–177, 2002.

    Article  Google Scholar 

  28. B. Schoelkopf and A. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2001.

    Google Scholar 

  29. C. Stauffer and W.E.L. Grimson. Adaptive background mixture models for real-time tracking. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 1998.

    Google Scholar 

  30. L. Sweeney. k-anonymity: A model for protecting privacy. International Journal on Uncertainty, Fuzziness, and Knowledge-Based Systems, 10(5):557–570, 2002.

    Article  MATH  MathSciNet  Google Scholar 

  31. S. Tansuriyavong and S-I. Hanaki. Privacy protection by concealing persons in circumstantial video image. In Proceedings of the 2001 Workshop on Perceptive User Interfaces, 2001.

    Google Scholar 

  32. D. Taubman and M. Marcellin. JPEG 2000: Image Compression Fundamentals, Standards and Practice. Kluwer Academic Publishers, 2002.

    Google Scholar 

  33. J.B. Tenenbaum and W. Freeman. Separating style and content with bilinear models. Neural Computation, 12(6):1247–1283, 2000.

    Article  Google Scholar 

  34. F. de la Torre and M. Black. A framework for robust subspace learning. International Journal of Computer Vision, 54(1–3):117–142, 2003.

    Article  MATH  Google Scholar 

  35. K. Toyama, J. Krumm, B. Brumitt, and B. Meyers. Wallflower: Principles and practice of background maintenance. In IEEE International Conference on Computer Vision, pages 255–261, 1999.

    Google Scholar 

  36. M. Vasilescu and D. Terzopoulous. Multilinear subspace analysis of image ensembles. In Computer Vision and Pattern Recognition, 2003.

    Google Scholar 

  37. P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2001.

    Google Scholar 

  38. J. Wickramasuriya, M. Alhazzazi, M. Datt, S. Mehrotra, and N. Venkatasubramanian. Privacy-protecting video surveillance. In SPIE International Symposium on Electronic Imaging (Real-Time Imaging IX), 2005.

    Google Scholar 

  39. C. Wren, A. Azarbayejani, T. Darrell, and A. Pentland. Pfinder: Real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Learning, 19(7): 780–785, 1997.

    Article  Google Scholar 

  40. Q. Zhao and J. Stasko. Evaluating image filtering based techniques in media space applications. In ACM Conference on Computer Supported Cooperative Work, pages 11–18, Seattle, WA, November 1998.

    Google Scholar 

Download references

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Latanya Sweeney
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jeffrey Cohn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fernando de la Torre
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Simon Baker
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ralph Gross .

Editor information

Editors and Affiliations

  1. Google Research, New York, USA

    Andrew Senior

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

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

Download citation

  • 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)

Publish with us

Policies and ethics

Search

Navigation