Understanding fake faces

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

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルComputer Vision – ECCV 2018 Workshops, Proceedings
編集者Laura Leal-Taixé, Stefan Roth
出版社Springer Verlag
ページ566-576
ページ数11
ISBN(印刷版)9783030110147
DOI
出版ステータスPublished - 2019
イベント15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
継続期間: 2018 9 82018 9 14

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11131 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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