Light field-based face liveness detection with convolutional neural networks
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Article number | 013003 |
Journal / Publication | Journal of Electronic Imaging |
Volume | 28 |
Issue number | 1 |
Online published | 8 Jan 2019 |
Publication status | Published - Jan 2019 |
Link(s)
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.
Research Area(s)
- convolutional neural networks, face liveness detection, face spoofing attack, light filed camera
Bibliographic Note
Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
Citation Format(s)
Light field-based face liveness detection with convolutional neural networks. / Liu, Mengyang; Fu, Hong; Wei, Ying et al.
In: Journal of Electronic Imaging, Vol. 28, No. 1, 013003, 01.2019.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review