Performance Evaluation of Face Anti-Spoofing Method Using Deep Metric Learning from a Few Frames of Face Video

Koichi Ito, Asateru Kimura, Takafumi Aoki

研究成果: Conference contribution

抄録

Recent advances in face recognition and deep learn-ing technologies are enabling us to identify individuals from images captured by a camera from a distance. On the other hand, there is a problem that a malicious person can impersonate the registered user by presenting a photo or video of the registered user's face. Spoofing detection using video input, from which more features can be extracted than images, has not been studied very much. In this paper, we propose a method for detecting spoofing from video images of a small number of frames. The proposed method uses features extracted from video images using 3D Convolutional Neural Network (3D CNN). We also use deep metric learning to improve the accuracy of detection. We demonstrate the effectiveness of the proposed method through performance evaluation experiments using a large-scale spoofing attack dataset.

本文言語English
ホスト出版物のタイトル2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1414-1419
ページ数6
ISBN(電子版)9789881476883
出版ステータスPublished - 2020 12 7
イベント2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Virtual, Auckland, New Zealand
継続期間: 2020 12 72020 12 10

出版物シリーズ

名前2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020 - Proceedings

Conference

Conference2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2020
国/地域New Zealand
CityVirtual, Auckland
Period20/12/720/12/10

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ ビジョンおよびパターン認識
  • ハードウェアとアーキテクチャ
  • 信号処理
  • 決定科学(その他)
  • 器械工学

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