Face liveness detection using convolutional-features fusion of real and deep network generated face images
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) | 574-582 |
Journal / Publication | Journal of Visual Communication and Image Representation |
Volume | 59 |
Online published | 13 Feb 2019 |
Publication status | Published - Feb 2019 |
Link(s)
Abstract
Conventionally, classifiers designed for face liveness detection are trained on real-world images, where real-face images and corresponding face presentation attacks (PA) are very much overlapped. However, a little research has been carried out in utilization of the combination of real-world face images and face images generated by deep convolutional neural networks (CNN) for face liveness detection. In this paper, we evaluate the adaptive fusion of convolutional-features learned by convolutional layers from real-world face images and deep CNN generated face images for face liveness detection. Additionally, we propose an adaptive convolutional-features fusion layer that adaptively balance the fusion of convolutional-features of real-world face images and face images generated by deep CNN during training. Our extensive experiments on the state-of-the-art face anti-spoofing databases, i.e., CASIA, OULU and Replay-Attack face anti-spoofing databases with both intra-database and cross-database scenarios indicate promising performance of the proposed method on face liveness detection compared to state-of-the-art methods.
Research Area(s)
- Adaptive fusion, Auto-encoder, Convolution neural networks, DNG face images, Face anti-spoofing, Face liveness detection
Citation Format(s)
Face liveness detection using convolutional-features fusion of real and deep network generated face images. / Rehman, Yasar Abbas Ur; Po, Lai-Man; Liu, Mengyang et al.
In: Journal of Visual Communication and Image Representation, Vol. 59, 02.2019, p. 574-582.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review