Advertisement

Privacy Visor: Method for Preventing Face Image Detection by Using Differences in Human and Device Sensitivity

  • Takayuki Yamada
  • Seiichi Gohshi
  • Isao Echizen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8099)

Abstract

A method is proposed for preventing unauthorized face image revelation through unintentional capture of facial images. Methods such as covering the face and painting particular patterns on the face effectively prevent detection of facial images but hinder face-to-face communication. The proposed method overcomes this problem through the use of a device worn on the face that transmits near-infrared signals that are picked up by camera image sensors, which makes faces in captured images undetectable. The device is similar in appearance to a pair of eyeglasses, and the signals cannot be seen by the human eye, so face-to-face communication is not hindered. Testing of a prototype ”privacy visor” showed that captured facial images are sufficiently corrupted to prevent unauthorized face image revelation by face detection.

Keywords

Privacy Unauthorized face image revelation Face detection Haar-like feature Near-infrared LED 

References

  1. 1.
    Cutillo, L., Molva, R.: Safebook: A privacy-preserving online social network leveraging on real-life trust. IEEE Communications Magazine 47(12), 94–101 (2009)CrossRefGoogle Scholar
  2. 2.
    Debatin, B., Lovejoy, J., Horn, A.: Facebook and Online Privacy: Attitudes, Behaviors, and Unintended Consequences. Journal of Computer-Mediated Communication 15(1), 83–108 (2009)CrossRefGoogle Scholar
  3. 3.
    Blackman, J.: Omniveillance, Google, Privacy in Public, and the Right to Your Digital Identity: A Tort for Recording and Disseminating an Individual’s Image Over the Internet. 49 Santa Clara Law Review 313, 341–392 (2009)Google Scholar
  4. 4.
  5. 5.
    Yamada, T., Gohshi, S., Echizen, I.: Preventing re-recording based on difference between sensory perceptions of humans and devices. In: Proc. of the 17th International Conference on Image Processing, ICIP 2010, pp. 993–996 (2010)Google Scholar
  6. 6.
  7. 7.
  8. 8.
    Feris, R.S., de Campos, T.E., Cesar Jr., R.M.: Detection and Tracking of Facial Features in Video Sequences. In: Cairó, O., Cantú, F.J. (eds.) MICAI 2000. LNCS, vol. 1793, pp. 127–135. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  9. 9.
    Viola, P., Jones, M.: Robust Real-Time Face Detection. International Journal of Computer Vision (IJCV) 57(2), 134–157 (2004)CrossRefGoogle Scholar
  10. 10.
    Schanda, J. (ed.): Colorimetry: Understanding the CIE System. Wiley-Interscience (2007)Google Scholar
  11. 11.
    Holst, G., Lomheim, T.: CMOS/CCD Sensors and Camera Systems. SPIE-International Society for Optical Engine (2007)Google Scholar
  12. 12.
    Bradski, G., Kaehler, A.: Learning Open CV Computer Vision with the Open CV Library. O’Reilly Media (2008)Google Scholar
  13. 13.
    Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection. In: Michaelis, B., Krell, G. (eds.) DAGM 2003. LNCS, vol. 2781, pp. 297–304. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Takayuki Yamada
    • 1
  • Seiichi Gohshi
    • 2
  • Isao Echizen
    • 1
  1. 1.National Institute of InformaticsJapan
  2. 2.Kogakuin UniversityJapan

Personalised recommendations