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Photorealistic Face De-Identification by Aggregating Donors’ Face Components

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9005))

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Abstract

With the adoption of pervasive surveillance systems and the development of efficient automatic face matchers, the question of preserving privacy becomes paramount. In this context, automated face de-identification is revived. Typical solutions based on eyes masking or pixelization, while commonly used in news broadcasts, produce very unnatural images. More sophisticated solutions were sparingly introduced in the literature, but they fail to account for fundamental constraints such as the visual likeliness of de-identified images. In contrast, we identify essential principles and build upon efficient techniques to derive an automated face de-identification solution meeting our predefined criteria. More specifically, our approach relies on a set of face donors from which it can borrow various face components (eyes, chin, etc.). Faces are then de-identified by substituting their own face components with the donors’ ones, in such a way that an automatic face matcher is fooled while the appearance of the generated faces are as close as possible to original faces. Experiments on several datasets validate the approach and show its ability both in terms of privacy preservation and visual quality.

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Acknowledgments

This work was partly supported by the ANR-SECULAR project.

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Authors and Affiliations

  1. CNRS UMR 6072, University of Caen, ENSICAEN, Caen, France

    Saleh Mosaddegh, Loic Simon & Frédéric Jurie

Authors
  1. Saleh Mosaddegh
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  2. Loic Simon
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  3. Frédéric Jurie
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Corresponding author

Correspondence to Saleh Mosaddegh .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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© 2015 Springer International Publishing Switzerland

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Mosaddegh, S., Simon, L., Jurie, F. (2015). Photorealistic Face De-Identification by Aggregating Donors’ Face Components. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9005. Springer, Cham. https://doi.org/10.1007/978-3-319-16811-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-16811-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16810-4

  • Online ISBN: 978-3-319-16811-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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