A 3D Mask Face Anti-Spoofing Database with Real World Variations

Siqi Liu, Baoyao Yang, Pong Chi YUEN, Guoying Zhao

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

25 Citations (Scopus)

Abstract

3D Mask face spoofing attack becomes new challenge and attracts more research interests in recent years. However, due to the deficiency number and limited variations of database, there are few methods be proposed to aim on it. Meanwhile, most of existing databases only concentrate on the anti-spoofing of different kinds of attacks and ignore the environmental changes in real world applications. In this paper, we build a new 3D mask anti-spoofing database with more variations to simulate the real world scenario. The proposed database contains 12 masks from two companies with different appearance quality. 7 Cameras from the stationary and mobile devices and 6 lighting settings that cover typical illumination conditions are also included. Therefore, each subject contains 42 (7 cameras ∗ 6 lightings) genuine and 42 mask sequences and the total size is 1008 videos. Through the benchmark experiments, directions of the future study are pointed out. We plan to release the database as an platform to evaluate methods under different variations.

Original languageEnglish
Title of host publicationProceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016
PublisherIEEE Computer Society
Pages1551-1557
Number of pages7
ISBN (Electronic)9781467388504
DOIs
Publication statusPublished - 16 Dec 2016
Event29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016 - Las Vegas, United States
Duration: 26 Jun 20161 Jul 2016

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference29th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2016
Country/TerritoryUnited States
CityLas Vegas
Period26/06/161/07/16

Scopus Subject Areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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