Investigation in Spatial-Temporal Domain for Face Spoof Detection

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

19 Scopus citations

Abstract

This paper focuses on face spoofing detection using video. The purpose is to find out the best scheme for this task in the end-to-end learning manner. We investigate 4 different types of structure to fully exploit the raw data in its spatial-temporal domain, which are the pure CNN, CNN with 3D convolution, CNN+LSTM and CNN+Conv-LSTM. Moreover, another stream built on optical flow is also used, and with a proper fusion method, it can improve the accuracy. In experiments, we compare schemes on the raw data in single stream and fusion methods with optical flow in two streams. The performance are not only given within each dataset, but also measured across different datsets, which is crucial to avoid the overfitting.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1538-1542
Number of pages5
ISBN (Print)9781538646588
DOIs
StatePublished - 10 Sep 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2018-April
ISSN (Print)1520-6149

Conference

Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country/TerritoryCanada
CityCalgary
Period15/04/1820/04/18

Keywords

  • CNN
  • Conv-LSTM
  • LSTM
  • Spoofing

Fingerprint

Dive into the research topics of 'Investigation in Spatial-Temporal Domain for Face Spoof Detection'. Together they form a unique fingerprint.

Cite this