  Face spoofing causes severe security threats in face recognition systems.
Previous anti-spoofing works focused on supervised techniques, typically with
either binary or auxiliary supervision. Most of them suffer from limited
robustness and generalization, especially in the cross-dataset setting. In this
paper, we propose a semi-supervised adversarial learning framework for spoof
face detection, which largely relaxes the supervision condition. To capture the
underlying structure of live faces data in latent representation space, we
propose to train the live face data only, with a convolutional Encoder-Decoder
network acting as a Generator. Meanwhile, we add a second convolutional network
serving as a Discriminator. The generator and discriminator are trained by
competing with each other while collaborating to understand the underlying
concept in the normal class(live faces). Since the spoof face detection is
video based (i.e., temporal information), we intuitively take the optical flow
maps converted from consecutive video frames as input. Our approach is free of
the spoof faces, thus being robust and general to different types of spoof,
even unknown spoof. Extensive experiments on intra- and cross-dataset tests
show that our semi-supervised method achieves better or comparable results to
state-of-the-art supervised techniques.
