Face liveness detection using convolutional-features fusion of real and deep network generated face images

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.