Dr. Fang Yuan Profile

Dr. Fang Yuan

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SPIE Journal Paper | 21 July 2016

Face liveness detection using shearlet-based feature descriptors

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

JEI, Vol. 25, Issue 04, 043014, (July 2016) https://doi.org/10.1117/12.10.1117/1.JEI.25.4.043014

KEYWORDS: Facial recognition systems, Databases, Video, Biometrics, Feature extraction, Photography, Detection and tracking algorithms, Image quality, Wavelets, Lithium

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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.

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