October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
Seeing Through De-Identified Faces in Videos by Humans and a Deep Convolutional Neural Network
Author Affiliations & Notes
  • Asal Baragchizadeh
    The University of Texas at Dallas
  • Kimberley D. Orsten-Hooge
    The University of Texas at Dallas
  • Thomas P. Karnowski
    Oak Ridge National Laboratory
  • David S. Bolme
    Oak Ridge National Laboratory
  • Regina Ferrell
    Oak Ridge National Laboratory
  • Parisa R. Jesudasen
    The University of Texas at Dallas
  • Carlos D. Castillo
    University of Maryland
  • Alice J. O’Toole
    The University of Texas at Dallas
  • Footnotes
    Acknowledgements  US Department of Energy - DE-AC05-00OR22725. Oak Ridge National Laboratory, CDC was supported by IARPA - 2014-14071600012.
Journal of Vision October 2020, Vol.20, 757. doi:https://doi.org/10.1167/jov.20.11.757
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      Asal Baragchizadeh, Kimberley D. Orsten-Hooge, Thomas P. Karnowski, David S. Bolme, Regina Ferrell, Parisa R. Jesudasen, Carlos D. Castillo, Alice J. O’Toole; Seeing Through De-Identified Faces in Videos by Humans and a Deep Convolutional Neural Network. Journal of Vision 2020;20(11):757. doi: https://doi.org/10.1167/jov.20.11.757.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

The increasing use of cameras in public spaces raises privacy concerns that have spurred the development of face de-identification methods. These methods are aimed at obscuring identity, while preserving facial actions. We evaluated the performance of eight face de-identification algorithms in naturalistic driving data. Humans and a pre-trained, high-performing deep convolutional neural network (DCNN) (Ranjan et al., 2018) were tested on their ability to “see through” the identity-masking methods. De-identification algorithms included a personalized supervised bilinear regression method for facial action transfer, a generic avatar face, four edge-detection methods, and two combined masking approaches. In an old/new recognition experiment, humans (n = 160) learned driver identities from high-resolution images and were tested with drivers in low-resolution videos. Faces in the videos were either intact or masked with one of the algorithms. Identification accuracy was lower in masked conditions (p < .001); (intact-face, d′ = 1.11; minimums: Canny edge-detection and avatar, d′ = .17 and .16, respectively). Subjects exhibited conservative decision bias for all videos (maximum: avatar, C = .74; minimum: Canny-inverted edge-detection, C = .18). Next, the DCNN was tested with the high-resolution images and frames extracted from videos. The output of the penultimate layer of the network served as a driver’s face representation. For each video, we created a driver identity template by averaging DCNN face representations across frames. Identification was measured as the cosine between the face representation vectors. Performance was tested between high-resolution images and each of the masked and unmasked video templates. The DCNN performed surprisingly well across conditions, given the challenging viewing conditions. Across conditions, there was general accord in the pattern of performance for humans and machines, with the best (unmasked) and worst (Canny edge-detection and avatar) conditions aligning. We propose that humans and machines should both be utilized for evaluating de-identification methods.

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