Skip to main content

Facial Spoofing Detection Using Temporal Texture Co-occurrence

Pan, Shi and Deravi, Farzin (2018) Facial Spoofing Detection Using Temporal Texture Co-occurrence. In: 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA). IEEE. ISBN 978-1-5386-2249-0. E-ISBN 978-1-5386-2248-3. (doi:10.1109/ISBA.2018.8311464)

PDF - Author's Accepted Manuscript
Download (312kB) Preview
[img]
Preview
Official URL
https://dx.doi.org/10.1109/ISBA.2018.8311464

Abstract

Biometric person recognition systems based on facial images are increasingly used in a wide range of applications. However, the potential for face spoofing attacks remains a significant challenge to the security of such systems and finding better means of detecting such presentation attacks has become a necessity. In this paper, we propose a new spoofing detection method, which is based on temporal changes in texture information. A novel temporal texture descriptor is proposed to characterise the pattern of change in a short video sequence named Temporal Co-occurrence Adjacent Local Binary Pattern (TCoALBP). Experimental results using the CASIA-FA, Replay Attack and MSU-MFSD datasets; the proposed method shows the effectiveness of the proposed technique on these challenging datasets.

Item Type: Book section
DOI/Identification number: 10.1109/ISBA.2018.8311464
Uncontrolled keywords: face; three-dimensional displays; feature extraction; image color analysis; histograms; video sequences; computational complexity
Divisions: Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Farzin Deravi
Date Deposited: 06 Apr 2018 10:56 UTC
Last Modified: 26 Sep 2019 11:49 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/66638 (The current URI for this page, for reference purposes)
Deravi, Farzin: https://orcid.org/0000-0003-0885-437X
  • Depositors only (login required):

Downloads

Downloads per month over past year