LiveNet : Improving features generalization for face liveness detection using convolution 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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Pages (from-to) | 159-169 |
Journal / Publication | Expert Systems with Applications |
Volume | 108 |
Online published | 8 May 2018 |
Publication status | Published - 15 Oct 2018 |
Link(s)
Abstract
Performance of face liveness detection algorithms in cross-database face liveness detection tests is one of the key issues in face-biometric based systems. Recently, Convolution Neural Networks (CNN) classifiers have shown remarkable performance in intra-database face liveness detection tests. However, a little effort has been made to improve the generalization capability of CNN classifiers for cross-database and unconstrained face liveness detection tests. In this paper, we propose an efficient strategy for training deep CNN classifiers for face liveness detection task. We utilize continuous data-randomization (like bootstrapping) in the form of small mini-batches during training CNN classifiers on small scale face anti-spoofing database. Experimental results revealed that the proposed approach reduces the training time by 18.39%, while significantly lowering the HTER by 8.28% and 14.14% in cross-database tests on CASIA-FASD and Replay-Attack database respectively as compared to state-of-the-art approaches. Additionally, the proposed approach achieves satisfactory results on intra-database and cross-database face liveness detection tests, claiming a good generality over other state-of-the-art face anti-spoofing approaches.
Research Area(s)
- Bootstrapping, Convolution neural networks, EER, Face anti-spoofing, Face liveness detection, Face-biometric, HTER, VGG-11
Citation Format(s)
LiveNet : Improving features generalization for face liveness detection using convolution neural networks. / Rehman, Yasar Abbas Ur; Po, Lai Man; Liu, Mengyang.
In: Expert Systems with Applications, Vol. 108, 15.10.2018, p. 159-169.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review