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Face anti-spoofing to 3D masks by combining texture and geometry features

  • Yan Wang
  • , Song Chen
  • , Weixin Li
  • , Di Huang*
  • , Yuhong Wang
  • *Corresponding author for this work
  • Beihang University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Anti-spoofing has become more important in face recognition systems. This paper proposes a novel approach to resist 3D face mask attacks, which jointly uses texture and shape features. Different from existing methods where depth information by extra equipments is required, we reconstruct geometry cues from RGB images through 3D Morphable Model. The hand-crafted features as well as the deep ones are then extracted to comprehensively represent texture and shape differences between real and fake faces and finally fused for decision making. The experiments are carried out on the 3D-MAD dataset and the competitive results indicate the effectiveness.

Original languageEnglish
Title of host publicationBiometric Recognition - 13th Chinese Conference, CCBR 2018, Proceedings
EditorsZhenan Sun, Shiguang Shan, Zhenhong Jia, Kurban Ubul, Jie Zhou, Jianjiang Feng, Zhenhua Guo, Yunhong Wang
PublisherSpringer Verlag
Pages399-408
Number of pages10
ISBN (Print)9783319979083
DOIs
StatePublished - 2018
Event13th Chinese Conference on Biometric Recognition, CCBR 2018 - Urumchi, China
Duration: 11 Aug 201812 Aug 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10996 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th Chinese Conference on Biometric Recognition, CCBR 2018
Country/TerritoryChina
CityUrumchi
Period11/08/1812/08/18

Keywords

  • 3D face reconstruction
  • Deep learning
  • Face anti-spoofing

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