Abstract
In this chapter, we present some studies on protecting personal identification in video. First, we discuss and evaluate automatic face masking techniques for obscuring human faces in video. Second, a user study is presented to reveal that face-masked video can be attacked using pair-wise constraints. Next, we propose an algorithm to show that this type of pair-wise constraint attack can be implemented using state-of-the-art machine learning approaches. Finally, a new obscuring approach is proposed to avoid the pair-wise constraint attack. The proposed approach protects people’s identity by obscuring the texture information of the entire body.
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Reference
Boyle, M., Edwards, C. and Greenberg, S. The Effects of Filtered Video on Awareness and Privacy. In Proc. of CSCW, 2000.
Brassil, J. Using Mobile Communications to Assert Privacy from Video Surveillance. In Proc. of IPDPS, 2005.
Cavallaro, A. Adding Privacy Constraints to Video-based Applications. European Workshop on the Integration of Knowledge, Semantics and Digital Media Technology, 2004.
Chen, D., Bharucha, A. and Wactlar, H. People Identification Through Ambient Camera Networks. International Conference on Multimedia and Ambient Intelligence, 2007.
Chen, D. and Yang, J. Online Learning Region Confidences for Object Tracking. Proc. Int. Conf. on Computer Vision workshop on Video Surveillance Performance Evaluation of Tracking and Surveillance, 2005.
Davis, J.W. and Bobick, A.F. The Representation and Recognition of Human Movement Using Temporal Templates. IEEE Proc. Computer Vision and Pattern Recognition, pp. 928–934, June 1997.
Decarlo, D. and Metaxas, D. Deformable Model Based Face Shape and Motion Estimation. Proc. Int’l Conf. Face and Gesture Recognition, 1996.
Elgammal, A., Duraiswami, R., Harwood, D. and Davis, L.S. Background and Foreground Modeling using Nonparametric Kernel Density Estimation for Visual Surveillance. Proc. of IEEE, vol. 7, no. 90, pp. 1151–1163, 2002.
Gelb, A. Applied Optimal Estimation. ed. MIT Press, 1992.
Gong, S., McKenna, S. and Collins, J.J. An Investigation into Face Pose Distributions. Proc. Int’l Conf. Automatic Face and Gesture Recognition, pp. 265–270, 1996.
Hager, G. and Toyama, K.X. Vision: A Portable Substrate for Real-Time Vision Applications. Computer Vision and Image Understanding, vol. 69, no. 1, pp. 23–37, 1998.
Hodgins, J.K., O’Brien, J.F. and Tumblin, J. Judgments of Human Motion with Different Geometric Models. IEEE: Trans. on Visualization and Computer Graphics, vol. 4, no. 4, December 1998.
Hudson, S. and Smith, I. Techniques for Addressing Fundamental Privacy and Disruption Tradeoffs in Awareness Support Systems. In Proc. of ACM Conference on Computer Supported Cooperative Work, 1996.
Kimeldorf, G. and Wahba, G. Some Results on Tchebycheffian Spline Functions. J. Math. Anal. Applic., vol. 33, pp. 82–95, 1971.
Lee, A. and Girgensohn, A. and Schlueter, K. NYNEX Portholes: Initial User Reactions and Redesign Implications. In Proc. of International Conference on Supporting Group Work, 1997.
Newton, E., Sweeney, L. and Malin, B. Preserving Privacy by De-identifying Facial Images. IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 2, February 2005, pp. 232–243.
Osuna, E., Freund, R. and Girosi, F. Training Support Vector Machines: An Application to Face Detection. Proc. Conf. Computer Vision and Pattern Recognition, pp. 130–136, January 1997.
Raja, Y., McKenna, S.J. and Gong, S. Tracking and Segmenting People in Varying Lighting Conditions Using Color. Proc. Int’l Conf. Automatic Face and Gesture Recognition, pp. 228–233, 1998.
Rowley, H.A., Baluja, S. and Kanade, T. Neural Networks Based Face Detection. IEEE Trans. Pattern Analysis an Machine Intelligence, vol. 20, no. 1, pp. 22–38, January 1998.
Schneiderman, H. and Kanade, T. A Statistical Method for 3D Object Detection Applied to Faces and Cars. Proc. Conf. Computer Vision and Pattern Recognition, vol. I, pp. 746–751, 2000.
Schwerdt, K. and Crowley, J. Robust Face Tracking Using Colour. Proc. Int’l Conf. Automatic Face and Gesture Recognition, pp. 90–95, 2000.
Senior, A., Pankanti, S., Hampapur, A., Brown, L., Tian, Y. and Ekin, A. Blinkering Surveillance: Enabling Video Privacy through Computer Vision. IBM Research Report, RC22886 (W0308-109), 2003.
Sung, K.K. and Poggio, T. Example-Based Learning for View-Based Human Face Detection. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 39–51, January 1998.
Tansuriyavong, S. and Hanaki, S. Privacy Protection by Concealing Persons in Circumstantial Video Image. In Proc. of PUI, 2001.
Terrillon, J.-C., Shirazi, M., Fukamachi, H. and Akamatsu, S. Comparative Performance of Different Skin Chrominance Models and Chrominance Spaces for the Automatic Detection of Human Faces in Color Images. Proc. Int’l Conf. Automatic Face and Gesture Recognition, pp. 54–61, 2000.
Viola, P. and Jones, M. Rapid Object Detection Using a Boosted Cascade of Simple Features. Proc. Conf. Computer Vision and Pattern Recognition, vol. I, pp. 511–518, 2001.
Wren, C., Azerbayejani, A., Darrell, T. and Pentland, A. Pfinder: A Real-Time Tracking of Human Body. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 780–785, July 1997.
Yan, R., Zhang, J., Yang J. and Hauptmann, A. A Discriminative Learning Framework with Pairwise Constraints for Video Object Classification. In Proc. of CVPR, 2004.
Zhang, W., Cheung, S. and Chen, M. Hiding Privacy Information in Video Surveillance System. In ICIP, 2005.
Zhao, Q. and Stasko, J. The Awareness-Privacy Tradeoff in Video Supported Informal Awareness: A Study of Image-Filtering Based Techniques. GVU Technical Report, GIT-GVU-98-16.
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Chen, D., Chang, Y., Yan, R., Yang, J. (2009). Protecting Personal Identification in Video. In: Senior, A. (eds) Protecting Privacy in Video Surveillance. Springer, London. https://doi.org/10.1007/978-1-84882-301-3_7
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DOI: https://doi.org/10.1007/978-1-84882-301-3_7
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