Understanding fake faces

Ryota Natsume, Kazuki Inoue, Yoshihiro Fukuhara, Shintaro Yamamoto, Shigeo Morishima, Hirokatsu Kataoka

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Face recognition research is one of the most active topics in computer vision (CV), and deep neural networks (DNN) are now filling the gap between human-level and computer-driven performance levels in face verification algorithms. However, although the performance gap appears to be narrowing in terms of accuracy-based expectations, a curious question has arisen; specifically, Face understanding of AI is really close to that of human? In the present study, in an effort to confirm the brain-driven concept, we conduct image-based detection, classification, and generation using an in-house created fake face database. This database has two configurations: (i) false positive face detections produced using both the Viola Jones (VJ) method and convolutional neural networks (CNN), and (ii) simulacra that have fundamental characteristics that resemble faces but are completely artificial. The results show a level of suggestive knowledge that indicates the continuing existence of a gap between the capabilities of recent vision-based face recognition algorithms and human-level performance. On a positive note, however, we have obtained knowledge that will advance the progress of face-understanding models.

    Original languageEnglish
    Title of host publicationComputer Vision – ECCV 2018 Workshops, Proceedings
    EditorsLaura Leal-Taixé, Stefan Roth
    PublisherSpringer-Verlag
    Pages566-576
    Number of pages11
    ISBN (Print)9783030110147
    DOIs
    Publication statusPublished - 2019 Jan 1
    Event15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
    Duration: 2018 Sep 82018 Sep 14

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11131 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference15th European Conference on Computer Vision, ECCV 2018
    CountryGermany
    CityMunich
    Period18/9/818/9/14

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    Keywords

    • Face recognition
    • False positives
    • Simulacra

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

    Cite this

    Natsume, R., Inoue, K., Fukuhara, Y., Yamamoto, S., Morishima, S., & Kataoka, H. (2019). Understanding fake faces. In L. Leal-Taixé, & S. Roth (Eds.), Computer Vision – ECCV 2018 Workshops, Proceedings (pp. 566-576). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11131 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-030-11015-4_42