Detecting presentation attacks from 3d face masks under multispectral imaging

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

9 Citations (Scopus)

Abstract

Automated detection of sensor level spoof attacks using 3D face masks is critical to protect integrity of face recognition systems deployed for security and surveillance. This paper investigates a multispectral imaging approach to more accurately detect such presentation attacks. Real human faces and spoof face images from 3D face masks are simultaneously acquired under visible and near infrared (multispectral) illumination using two separate sensors. Ranges of convolutional neural network based configurations are investigated to improve the detection accuracy from such presentation attacks. Our experimental results indicate that near-infrared based imaging of 3D face masks offers superior performance as compared to those for the respective real/spoof face images acquired under visible illumination. Combination of simultaneously acquired presentation attack images under multispectral illumination can be used to further improve the accuracy of detecting attacks from more realistic 3D face masks.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
PublisherIEEE Computer Society
Pages47-52
Number of pages6
ISBN (Electronic)9781538661000
DOIs
Publication statusPublished - 13 Dec 2018
Event31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 - Salt Lake City, United States
Duration: 18 Jun 201822 Jun 2018

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2018-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
Country/TerritoryUnited States
CitySalt Lake City
Period18/06/1822/06/18

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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