conversations.from: human
conversations.value: What could be a title for a book about Markus, Burki, Computing with the genre: Computers & Technology, Computer Science?
conversations.from: assistant
conversations.value: A title for this book could be 'Handbook of Face Recognition'
conversations.from: human
conversations.value: Do you know the first three paragraphs for the book?
conversations.from: assistant
conversations.value: Handbook of Face Recognition
  Most recent work on visual privacy protection has focused on systems that modify, or redact, visual images to mask out privacy sensitive information. Such systems typically use computer vision technology to determine privacy sensitive regions of the image, for instance tracking moving people in a video [51], or detecting faces [22] in still or moving images. Such regions of interest are then changed in some way to prevent subsequent viewers or algorithms from extracting the privacy sensitive information. Obscuration methods that are commonly used include blurring, masking, pixellating [27], scrambling [18], or permuting pixels [12]. Recent work has investigated the limitations of some of these, for instance Gross et al. [25] show that simple pixellation and blurring may not be strong enough to defeat face recognition systems. They train a parrot [37] recognizer on gallery images with the same distortion as the probe and obtain markedly higher recognition rates than using a system trained on clean images. Neustaedter et al. [35] have also found global blurring and other obscuration techniques to be unable to supply simultaneously both sufficient privacy and adequate information for always-on home video conferencing. Koshimizu et al. [31] have explored the acceptability of different obscuration and rerendering techniques for video surveillance.
  Stronger masking with greater changes to the image may have the limitation of reducing the usability of the video for its intended purpose, but rerendering [51] may alleviate this by showing computer generated images to convey important information hidden by the redaction process. One example of this would be to obscure a person's face in an image with a computer generated face--hiding the identity yet preserving the gaze direction and expression. Two extensions of this using face modeling are described in Sect. 27.5.2.
  One important aspect of redaction systems is reversibility. It may be desirable for some purposes to permanently destroy the privacy-intrusive information, but for others it may desirable or necessary, perhaps for evidential reasons, to be able to reconstruct the original video.
conversations.from: human
conversations.value: Delete the last sentence.
conversations.from: assistant
conversations.value: Handbook of Face Recognition
  Most recent work on visual privacy protection has focused on systems that modify, or redact, visual images to mask out privacy sensitive information. Such systems typically use computer vision technology to determine privacy sensitive regions of the image, for instance tracking moving people in a video [51], or detecting faces [22] in still or moving images. Such regions of interest are then changed in some way to prevent subsequent viewers or algorithms from extracting the privacy sensitive information. Obscuration methods that are commonly used include blurring, masking, pixellating [27], scrambling [18], or permuting pixels [12]. Recent work has investigated the limitations of some of these, for instance Gross et al. [25] show that simple pixellation and blurring may not be strong enough to defeat face recognition systems. They train a parrot [37] recognizer on gallery images with the same distortion as the probe and obtain markedly higher recognition rates than using a system trained on clean images. Neustaedter et al. [35] have also found global blurring and other obscuration techniques to be unable to supply simultaneously both sufficient privacy and adequate information for always-on home video conferencing. Koshimizu et al. [31] have explored the acceptability of different obscuration and rerendering techniques for video surveillance.
  Stronger masking with greater changes to the image may have the limitation of reducing the usability of the video for its intended purpose, but rerendering [51] may alleviate this by showing computer generated images to convey important information hidden by the redaction process. One example of this would be to obscure a person's face in an image with a computer generated face--hiding the identity yet preserving the gaze direction and expression. Two extensions of this using face modeling are described in Sect. 27.5.2.
  One important aspect of redaction systems is reversibility.
conversations.from: human
conversations.value: Do you know four more paragraphs?
conversations.from: assistant
conversations.value: Dufaux, F., Ebrahimi, T.: Scrambling for video surveillance with privacy. In: Proceedings of Computer Vision and Pattern Recognition, June 2006, p. 160 (2006)
  Erkin, Z., Franz, M., Guajardo, J., Katzenbeisser, S., Lagendijk, I., Toft, T.: Privacy-preserving face recognition. In: Privacy Enhancing Technologies Symposium (2009)
  Flores, A., Belongie, S.: Removing pedestrians from Google Street View images. In: International Workshop on Mobile Vision, June 2010
  Frome, A., Cheung, G., Abdulkader, A., Zennaro, M., Wu, B., Bissacco, A., Adam, H., Neven, H., Vincent, L.: Large-scale privacy protection in Google Street View. In: Proceedings of Computer Vision and Pattern Recognition (2009)
conversations.from: human
conversations.value: Do you know the last five paragraphs?
conversations.from: assistant
conversations.value: Wickramasuriya, J., Alhazzazi, M., Datt, M., Mehrotra, S., Venkatasubramanian, N.: Privacy-protecting video surveillance. In: SPIE International Symposium on Electronic Imaging (2005)
  Yu, T., Lim, S.-N., Patwardhan, K., Krahnstoever, N.: Monitoring, recognizing and discovering social networks. In: Proceedings of Computer Vision and Pattern Recognition (2009)
  Zhang, W., Cheung, S.-C., Chen, M.: Hiding privacy information in video surveillance system. In: Proc. International Conference on Image Processing (2005)
  Consider the case of the British fraudster John Darwin who faked his own death but was identified in a photograph on a real estate web site after subsequently buying property [61].
  Stan Z. Li and Anil K. Jain (eds.)Second Edition10.1007/978-0-85729-932-1(C) Springer-Verlag London Limited 2011