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- Title
Light field-based face liveness detection with convolutional neural networks.
- Authors
Liu, Mengyang; Fu, Hong; Wei, Ying; Rehman, Yasar Abbas Ur; Po, Lai-man; Lo, Wai Lun
- 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.
- Subjects
HUMAN facial recognition software; ARTIFICIAL neural networks; SCIENCE journalism; LIGHT-field cameras; PATTERN recognition systems; LIGHT
- Publication
Journal of Electronic Imaging, 2019, Vol 28, Issue 1, p1
- ISSN
1017-9909
- Publication type
Article
- DOI
10.1117/1.JEI.28.1.013003