Face liveness detection using shearlet-based feature descriptors

Lai-Man Po; Yuming Li; Fang Yuan; Litong Feng

doi:10.1117/1.JEI.25.4.043014

21 July 2016 Face liveness detection using shearlet-based feature descriptors

Lai-Man Po, Yuming Li, Fang Yuan, Litong Feng

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 4, 043014 (July 2016). https://doi.org/10.1117/1.JEI.25.4.043014

Abstract

Face recognition is a widely used biometric technology due to its convenience but it is vulnerable to spoofing attacks made by nonreal faces such as photographs or videos of valid users. The antispoof problem must be well resolved before widely applying face recognition in our daily life. Face liveness detection is a core technology to make sure that the input face is a live person. However, this is still very challenging using conventional liveness detection approaches of texture analysis and motion detection. The aim of this paper is to propose a feature descriptor and an efficient framework that can be used to effectively deal with the face liveness detection problem. In this framework, new feature descriptors are defined using a multiscale directional transform (shearlet transform). Then, stacked autoencoders and a softmax classifier are concatenated to detect face liveness. We evaluated this approach using the CASIA Face antispoofing database and replay-attack database. The experimental results show that our approach performs better than the state-of-the-art techniques following the provided protocols of these databases, and it is possible to significantly enhance the security of the face recognition biometric system. In addition, the experimental results also demonstrate that this framework can be easily extended to classify different spoofing attacks.

Citation Download Citation

Lai-Man Po, Yuming Li, Fang Yuan, and Litong Feng "Face liveness detection using shearlet-based feature descriptors," Journal of Electronic Imaging 25(4), 043014 (21 July 2016). https://doi.org/10.1117/1.JEI.25.4.043014

Published: 21 July 2016

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available