Face De-identification

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 naive 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.

[1]  Michael W. Marcellin,et al.  JPEG2000 - image compression fundamentals, standards and practice , 2002, The Kluwer International Series in Engineering and Computer Science.

[2]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[3]  Isabel Martinez-Ponte,et al.  ROBUST HUMAN FACE HIDING ENSURING PRIVACY , 2004 .

[4]  Ralph Gross,et al.  Face de-identification using multi-factor active appearance models , 2008 .

[5]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[6]  Takeo Kanade,et al.  Object Detection Using the Statistics of Parts , 2004, International Journal of Computer Vision.

[7]  Tomoji Toriyama,et al.  Factors on the sense of privacy in video surveillance , 2006, CARPE '06.

[8]  James L. Crowley,et al.  Perceptual user interfaces: things that see , 2000, CACM.

[9]  Carman Neustaedter,et al.  Blur filtration fails to preserve privacy for home-based video conferencing , 2006, TCHI.

[10]  John T. Stasko,et al.  Evaluating image filtering based techniques in media space applications , 1998, CSCW '98.

[11]  Mohan Trivedi,et al.  The networked sensor tapestry (NeST): a privacy enhanced software architecture for interactive analysis of data in video-sensor networks , 2004, VSSN '04.

[12]  Simon Baker,et al.  Active Appearance Models Revisited , 2004, International Journal of Computer Vision.

[13]  Takeo Kanade,et al.  Multi-PIE , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[14]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Demetri Terzopoulos,et al.  Multilinear subspace analysis of image ensembles , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[16]  Bradley Malin,et al.  Preserving privacy by de-identifying face images , 2005, IEEE Transactions on Knowledge and Data Engineering.

[17]  Tsuhan Chen,et al.  The painful face - Pain expression recognition using active appearance models , 2009, Image Vis. Comput..

[18]  Nalini Venkatasubramanian,et al.  Privacy-protecting video surveillance , 2005, IS&T/SPIE Electronic Imaging.

[19]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[20]  Takeo Kanade,et al.  CareMedia: Automated Video and Sensor Analysis for Geriatric Care , 2007 .

[21]  Ralph Gross,et al.  Semi-supervised learning of multi-factor models for face de-identification , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Christopher Edwards,et al.  The effects of filtered video on awareness and privacy , 2000, CSCW '00.

[23]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[24]  Bruce A. Draper,et al.  The CSU Face Identification Evaluation System , 2005, Machine Vision and Applications.

[25]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[26]  Michael J. Black,et al.  A Framework for Robust Subspace Learning , 2003, International Journal of Computer Vision.

[27]  H. Pluckrose,et al.  Things to See , 1973 .

[28]  J. Navarro-Pedreño.  Numerical Methods for Least Squares Problems , 1996 .

[29]  Kurt Hornik,et al.  Neural networks and principal component analysis: Learning from examples without local minima , 1989, Neural Networks.

[30]  L. Sweeney,et al.  Preserving Privacy by De-identifying Facial Images , 2003 .

[31]  Shin-ichi Hanaki,et al.  Privacy protection by concealing persons in circumstantial video image , 2001, PUI '01.

[32]  Rong Yan,et al.  People Identification with Limited Labels in Privacy-Protected Video , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[33]  Scott E. Hudson,et al.  Techniques for addressing fundamental privacy and disruption tradeoffs in awareness support systems , 1996, CSCW '96.

[34]  Mourad Ouaret,et al.  Privacy enabling technology for video surveillance , 2006, SPIE Defense + Commercial Sensing.

[35]  Edoardo M. Airoldi,et al.  Integrating Utility into Face De-identification , 2005, Privacy Enhancing Technologies.

[36]  Kentaro Toyama,et al.  Wallflower: principles and practice of background maintenance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[37]  Ralph Gross,et al.  Model-Based Face De-Identification , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[38]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[39]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[40]  Joshua B. Tenenbaum,et al.  Separating Style and Content with Bilinear Models , 2000, Neural Computation.

[41]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[42]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[43]  Carman Neustaedter,et al.  Balancing Privacy and Awareness in Home Media Spaces 1 , 2003 .