Light field-based face liveness detection with convolutional neural networks

Mengyang Liu; Hong Fu; Ying Wei; Yasar Abbas Ur Rehman; Lai-Man Po; Wai Lun Lo

doi:10.1117/1.JEI.28.1.013003

8 January 2019 Light field-based face liveness detection with convolutional neural networks

Mengyang Liu, Hong Fu, Ying Wei, Yasar Abbas Ur Rehman, Lai-Man Po, Wai Lun Lo

Author Affiliations +

Funded by: Research Grants Council, University Grants Committee, Key Research and Development Plan of Shandong Province

Journal of Electronic Imaging, Vol. 28, Issue 1, 013003 (January 2019). https://doi.org/10.1117/1.JEI.28.1.013003

Abstract

Face recognition based-access systems have been used widely in security systems as the recognition accuracy can be quite high. However, these systems suffer from low robustness to spoofing attacks. To achieve a reliable security system, a well-defined face liveness detection technique is crucial. We present an approach for this problem by combining data of the light-field camera (LFC) and the convolutional neural networks in the detection process. The LFC can detect the depth of an object by a single shot, from which we derive meaningful features to distinguish the spoofing attack from the real face, through a single shot. We propose two features for liveness detection: the ray difference images and the microlens images. Experimental results based on a self-built light-field imaging database for three types of the spoofing attacks are presented. The experimental results show that the proposed system gives a lower average classification error (0.028) as compared with the method of using hand-crafted features and conventional imaging systems. In addition, the proposed system can be used to classify the type of the spoofing attack.

Citation Download Citation

Mengyang Liu, Hong Fu, Ying Wei, Yasar Abbas Ur Rehman, Lai-Man Po, and Wai Lun Lo "Light field-based face liveness detection with convolutional neural networks," Journal of Electronic Imaging 28(1), 013003 (8 January 2019). https://doi.org/10.1117/1.JEI.28.1.013003

Received: 21 June 2018; Accepted: 5 December 2018; Published: 8 January 2019

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