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EURASIP Journal on Advances in Signal Processing
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Tools for Protecting the Privacy of Specific Individuals in Video

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 075427 (2007) Cite this article

Abstract

This paper presents a system for protecting the privacy of specific individuals in video recordings. We address the following two problems: automatic people identification with limited labeled data, and human body obscuring with preserved structure and motion information. In order to address the first problem, we propose a new discriminative learning algorithm to improve people identification accuracy using limited training data labeled from the original video and imperfect pairwise constraints labeled from face obscured video data. We employ a robust face detection and tracking algorithm to obscure human faces in the video. Our experiments in a nursing home environment show that the system can obtain a high accuracy of people identification using limited labeled data and noisy pairwise constraints. The study result indicates that human subjects can perform reasonably well in labeling pairwise constraints with the face masked data. For the second problem, we propose a novel method of body obscuring, which removes the appearance information of the people while preserving rich structure and motion information. The proposed approach provides a way to minimize the risk of exposing the identities of the protected people while maximizing the use of the captured data for activity/behavior analysis.

References

  1. Senior A, Pankanti S, Hampapur A, Brown L, Tian Y-L, Ekin A: Blinkering surveillance: enabling video privacy through computer vision . In Tech. Rep. RC22886 (W0308-109). IBM, White Plains, NY, USA; 2003.

    Google Scholar 

  2. Tansuriyavong S, Hanaki S-I: Privacy protection by concealing persons in circumstantial video image. Proceedings of the Workshop on Perceptive User Interfaces (PUI '01), November 2001, Orlando, Fla, USA 1–4.

    Google Scholar 

  3. Brassil J: Using mobile communications to assert privacy from video surveillance. Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS '05), April 2005, Denver, Colo, USA 290.

    Google Scholar 

  4. Zhang W, Cheung S-CS, Chen M: Hiding privacy information in video surveillance system. Proceedings of International Conference on Image Processing (ICIP '05), September 2005, Genova, Italy 3: 868–871.

    Google Scholar 

  5. Hudson SE, Smith I: Techniques for addressing fundamental privacy and disruption tradeoffs in awareness support systems. Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW '96), November 1996, Boston, Mass, USA 248–257.

    Google Scholar 

  6. Lee A, Girgensohn A, Schlueter K: NYNEX portholes: initial user reactions and redesign implications. Proceedings of the International ACM SIGGROUP Conference on Supporting Group Work (GROUP '97), November 1997, Phoenix, Ariz, USA 385–394.

    Chapter  Google Scholar 

  7. Zhao Q, Stasko J: The awareness-privacy tradeoff in video supported informal awareness: a study of image-filtering based techniques. In Tech. Rep. GIT-GVU-98-16. Graphics, Visualization, and Usability Center, Atlanta, Ga, USA; 1998.

    Google Scholar 

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

    Article  Google Scholar 

  9. Boyle M, Edwards C, Greenberg S: The effects of filtered video on awareness and privacy. Proceedings of the ACM Conference on Computer Supported Cooperative Work (CSCW '00), December 2000, Philadelphia, Pa, USA 1–10.

    Google Scholar 

  10. Terrillon J-C, Shirazi MN, Fukamachi H, Akamatsu S: Comparative performance of different skin chrominance models and chrominance spaces for the automatic detection of human faces in color images. Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition, March 2000, Grenoble, France 54–61.

    Google Scholar 

  11. Chen D, Yang J: Online learning of region confidences for object tracking. Proceedings of the 2nd Joint IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS '05), October 2005, Beijing, China 1–8.

    Google Scholar 

  12. Sung K-K, Poggio T: Example-based learning for view-based human face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998,20(1):39-51. 10.1109/34.655648

    Article  Google Scholar 

  13. Rowley HA, Baluja S, Kanade T: Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998,20(1):23-38. 10.1109/34.655647

    Article  Google Scholar 

  14. Osuna E, Freund R, Girosi F: Training support vector machines: an application to face detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '97), June 1997, San Juan, Puerto Rico, USA 130–136.

    Chapter  Google Scholar 

  15. Viola P, Jones M: Rapid object detection using a boosted cascade of simple features. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01), December 2001, Kauai, Hawaii, USA 1: 511–518.

    Google Scholar 

  16. Schneiderman H, Kanade T: A statistical method for 3D object detection applied to faces and cars. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '00), June 2000, Hilton Head Island, SC, USA 1: 746–751.

    Google Scholar 

  17. Gong S, McKenna S, Collins JJ: An investigation into face pose distributions. Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition, October 1996, Killington, Vt, USA 265–270.

    Chapter  Google Scholar 

  18. Hager GD, Toyama K: X vision: a portable substrate for real-time vision applications. Computer Vision and Image Understanding 1998,69(1):23-37. 10.1006/cviu.1997.0586

    Article  Google Scholar 

  19. Raja Y, McKenna SJ, Gong S: Tracking and segmenting people in varying lighting conditions using colour. Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition, April 1998, Nara, Japan 228–233.

    Chapter  Google Scholar 

  20. Schwerdt K, Crowley JL: Robust face tracking using color. Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition, March 2000, Grenoble, France 90–95.

    Google Scholar 

  21. Wren CR, Azarbayejani A, Darrell T, Pentland AP: Pfinder: real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997,19(7):780-785. 10.1109/34.598236

    Article  Google Scholar 

  22. Gelb A (Ed): Applied Optimal Estimation. MIT Press, Cambridge, Mass, USA; 1992.

    Google Scholar 

  23. Elgammal A, Duraiswami R, Harwood D, Davis LS: Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proceedings of the IEEE 2002,90(7):1151-1163. 10.1109/JPROC.2002.801448

    Article  Google Scholar 

  24. Yan R, Zhang J, Yang J, Hauptmann A: A discriminative learning framework with pairwise constraints for video object classification. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '04), June-July 2004, Washington, DC, USA 2: 284–293.

    Google Scholar 

  25. Kimeldorf G, Wahba G: Some results on Tchebycheffian spline functions. Journal of Mathematical Analysis and Applications 1971,33(1):82-95. 10.1016/0022-247X(71)90184-3

    Article  MathSciNet  Google Scholar 

  26. Hodgins JK, O'Brien JF, Tumblin J: Perception of human motion with different geometric models. IEEE Transactions on Visualization and Computer Graphics 1998,4(4):307-316. 10.1109/2945.765325

    Article  Google Scholar 

  27. Davis JW, Bobick AF: The representation and recognition of human movement using temporal templates. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '97), June 1997, San Juan, Puerto Rico, USA 928–934.

    Chapter  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Datong Chen, Yi Chang, Rong Yan & Jie Yang

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  1. Datong Chen
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  2. Yi Chang
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  3. Rong Yan
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  4. Jie Yang
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Correspondence to Datong Chen.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Chen, D., Chang, Y., Yan, R. et al. Tools for Protecting the Privacy of Specific Individuals in Video. EURASIP J. Adv. Signal Process. 2007, 075427 (2007). https://doi.org/10.1155/2007/75427

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  • DOI: https://doi.org/10.1155/2007/75427

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