Multimedia Tools and Applications

, Volume 76, Issue 3, pp 3435–3468 | Cite as

De-identifying facial images using singular value decomposition and projections

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Abstract

In this paper, two methods are presented that manipulate images to hinder automatic face identification. They partly degrade image quality, so that humans can identify the persons in a scene, while face identification algorithms fail to do so. The approaches used involve: a) singular value decomposition (SVD) and b) image projections on hyperspheres. Simulation experiments verify that these methods reduce the percentage of correct face identification rate by over 90 %. Additionally, the final image is not degraded beyond recognition by humans, in contrast with the majority of other de-identification methods.

Keywords

Face de-idenification Privacy protection Singular value decomposition Projections on hyperspheres 

References

  1. 1.
    Agrawal PR, Narayanan PJ (2005) Person De-identification in Videos. In: IEEE Transactions on Circuits and Systems for Video Technology, vol 21, pp 299–310Google Scholar
  2. 2.
    Bitouk D, Kumar N, Dhillon S, Belhumeur PN, Nayar SK (2008) Face Swapping: Automatically Replacing Faces in Photographs, ACM Trans. on Graphics (also Proc. of ACMSIGGRAPH)Google Scholar
  3. 3.
    Blanz V, Romdhani S, Vetter T (2002) Face identification across different poses and illuminations with a 3D morphable model. In: Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition, 2002, pp 192–197Google Scholar
  4. 4.
    Boyd S, Vandenberghe L (2004) Convex Optimization:244Google Scholar
  5. 5.
    Du L, Yi M, Blasch E, Ling H (2014) GARP-Face: Balancing Privacy Protection and Utility Preservation in Face De-identification. In: Proceedings of IEEE International Joint Conference on Biometrics (IJCB)Google Scholar
  6. 6.
    Driessen B, Drmuth M (2013) Achieving anonymity against major face identification algorithms. In: Cryptology ePrint ArchiveGoogle Scholar
  7. 7.
    Georghiades A, Belhumeur P, Kriegman’s D (2001) From few to many: illumination cone models for face identification under variable lighting and pose. In: PAMIGoogle Scholar
  8. 8.
    Golub GH, Van Loan CF (2012) Matrix Computations, 4th Edn. The Johns Hopkins University Press, Baltimore, pp 76–81. ISBN 13: 978- 1-4214-0794-4Google Scholar
  9. 9.
    Gross R, Airoldi E, Malin B, Sweeney L (2006) Integrating Utility into Face De-Identification, Privacy Enhancing Technologies, Volume 3856 of the series Lecture Notes in Computer Science pp 227–242Google Scholar
  10. 10.
    Gross R, Sweeney L, Cohn J, de la Torre F, Baker S (2009) Face De-Identification. In: Protecting Privacy in Video Surveillance, pp 129–146Google Scholar
  11. 11.
    Gross R, Sweeney L, de la Torre F, Baker S (2008) Semi-Supervised Learning of Multi-Factor Models for Face De-Identification. In: IEEE Conference on Computer Vision and Pattern Recognition, 2008, CVPR 2008, pp 1–8Google Scholar
  12. 12.
    Iosifidis A, Tefas A, Pitas I (2013) Person Identification from Actions based on Dynemes and Discriminant Learning. In: IEEE International Workshop on Biometrics and Forensics (IWBF), LisbonGoogle Scholar
  13. 13.
    Jourabloo A, Yin X, Liu X (2015) Attribute preserved face de-identification. In: 2015 International Conference on Biometrics (ICB). IEEEGoogle Scholar
  14. 14.
    Letournel G, Bugeau A, Ta V-T, Domenger J-P (2015) Face de-identification with expressions preservation. In: IEEE International Conference on Image Processing (ICIP). IEEEGoogle Scholar
  15. 15.
    Meng L, Zongji S (2014) Face De-identification with perfect privacy protection. In: 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). IEEEGoogle Scholar
  16. 16.
    Meng L, Zongji S, Ariyaeeinia A, Bennett KL (2014) Retaining expressions on de-identified faces. In: 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO). IEEEGoogle Scholar
  17. 17.
    Messer K, Matas J, Kittler J, Luettin J, Maitre G (1999) XM2VTSbd: The Extended M2VTS Database. In: Proceedings 2nd Conference on Audio and Video-base Biometric Personal Verification (AVBPA99). Springer, New YorkGoogle Scholar
  18. 18.
    Mosaddegh S, Simon L, Jurie F Photorealistic Face de-Identification by Aggregating Donors’ Face Components. In: Asian Conference on Computer Vision, Singapore, pp 1–16Google Scholar
  19. 19.
    Newton E, Sweeney L, Mali B (2005) Preserving Privacy by De-identifying Facial Images. In: IEEE Transactions on Knowledge and Data Engineering, pp 232–243Google Scholar
  20. 20.
    Pitas I (2000) Digital Image processing algorithms and applications, ISBN: 978-0-471-37739-9Google Scholar
  21. 21.
    Phillips PJ (2013) Privacy Operating Characteristic for Privacy Protection in Surveillance Applications. In: Audio- and Video-Based Biometric Person Authentication, pp 869–878Google Scholar
  22. 22.
    Singular Value Decomposition (SVD), Department of Computer Science & Engineering University of Nevada, CS4/791Y: Mathematical Methods for Computer Vision, Dr. George Bebis http://www.cse.unr.edu/bebis/MathMethods/SVD/lecture.pdf
  23. 23.
    Singular Value Decomposition (SVD) tutorial MIT BE.400 / 7.548, Perspectives in Biological Engineering Course http://web.mit.edu/be.400/www/SVD/Singular_Value_Decomposition.htm
  24. 24.
    Sommerville DMY (1958) An Introduction to the Geometry of n Dimensions, MethuenGoogle Scholar
  25. 25.
    Stamou G, Krinidis M, Nikolaidis N, Pitas I (2005) A monocular system for automatic face detection and tracking. In: Proceedings of Visual Communications and Image Processing (VCIP 2005), Beijing, pp 12–15Google Scholar
  26. 26.
    Sweeney L (2002) K-anonymity: a model for protecting privacy. International Journal on Uncertainty,Fuzziness and Knowledge-based Systems 10(5):557–570MathSciNetCrossRefMATHGoogle Scholar
  27. 27.
    Tansuriyavong S, Hanaki S (2001) Privacy protection by concealing persons in circumstantial video image. In: Proceedings of the 2001 workshop on Perceptive user interfaces, pp 1–4Google Scholar
  28. 28.
    Tax DMJ, Duin RPW (2004) Support Vector Data Description. Mach Learn 54:45–66CrossRefMATHGoogle Scholar
  29. 29.
    Theodoridis S, Slavakis K, Yamada I (2011) Adaptive Learning in a World of Projections. IEEE Signal Proc Mag:97–123Google Scholar
  30. 30.
    Weisstein EW (2014) Hypersphere, MathWorld, A Wolfram Web Resource. http://mathworld.wolfram.com/Hypersphere.html
  31. 31.
    Zoidi O, Nikolaidis N, Pitas I (2013) Exploiting clustering and stereo information in label propagation of facial images. In: IEEE symposium series on computational intelligence (SSCI 2013), Singapore, pp 16–19Google Scholar
  32. 32.
    Zongji S, Meng L, Ariyaeeinia L (2015) Distinguishable de-identified faces. In: 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol 4. IEEEGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of InformaticsAristotle University of ThesslonikiThessalonikiGreece
  2. 2.Department of Electrical and Electronic EngineeringUniversity of BristolBristolUK

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