Skip to main content

Facial Action Units for Presentation Attack Detection

Shi, Pan, Deravi, Farzin (2017) Facial Action Units for Presentation Attack Detection. In: 2017 Seventh International Conference on Emerging Security Technologies (EST). . IEEE ISBN 978-1-5386-4019-7. E-ISBN 978-1-5386-4018-0. (doi:10.1109/EST.2017.8090400) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

PDF - Publisher pdf
Restricted to Repository staff only
Contact us about this Publication Download (348kB)
[img]
Official URL
https://doi.org/10.1109/EST.2017.8090400

Abstract

This paper is concerned with biometric spoofing detection using the dynamics of natural facial movements as a feature. Facial muscle movement information can be extracted from video sequences and encoded using the Facial Action Coding System (FACS). The proposed feature constructs a Facial Action Units Histogram (FAUH) to encapsulate this information for the detection of biometric presentation attacks without the need for active user cooperation. The performance of the proposed system was tested on two datasets: CASIA-FASD and Replay Attack and produced encouraging results. Further improvements may be possible by integrating this source of information with other indicators for further protecting biometric systems from subversion.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/EST.2017.8090400
Uncontrolled keywords: biometric, face, anti-spoofing, facial action unit
Subjects: Q Science
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Image and Information Engineering
Depositing User: Farzin Deravi
Date Deposited: 09 Sep 2017 10:26 UTC
Last Modified: 29 May 2019 19:30 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/63351 (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