Deep convolutional dynamic texture learning with adaptive channel-discriminability for 3D mask face anti-spoofing

Rui Shao, Xiangyuan LAN, Pong Chi YUEN

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

25 Citations (Scopus)

Abstract

3D mask spoofing attack has been one of the main challenges in face recognition. A real face displays a different motion behaviour compared to a 3D mask spoof attempt, which is reflected by different facial dynamic textures. However, the different dynamic information usually exists in the subtle texture level, which cannot be fully differentiated by traditional hand-crafted texture-based methods. In this paper, we propose a novel method for 3D mask face anti-spoofing, namely deep convolutional dynamic texture learning, which learns robust dynamic texture information from fine-grained deep convolutional features. Moreover, channel-discriminability constraint is adaptively incorporated to weight the discriminability of feature channels in the learning process. Experiments on both public datasets validate that the proposed method achieves promising results under intra and cross dataset scenario.

Original languageEnglish
Title of host publicationIEEE International Joint Conference on Biometrics, IJCB 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages748-755
Number of pages8
ISBN (Electronic)9781538611241
DOIs
Publication statusPublished - 29 Jan 2018
Event2017 IEEE International Joint Conference on Biometrics, IJCB 2017 - Denver, United States
Duration: 1 Oct 20174 Oct 2017

Publication series

NameIEEE International Joint Conference on Biometrics, IJCB 2017
Volume2018-January

Conference

Conference2017 IEEE International Joint Conference on Biometrics, IJCB 2017
Country/TerritoryUnited States
CityDenver
Period1/10/174/10/17

Scopus Subject Areas

  • Computer Networks and Communications
  • Instrumentation
  • Signal Processing
  • Biomedical Engineering

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