  Face anti-spoofing is crucial for the security of face recognition systems.
Learning based methods especially deep learning based methods need large-scale
training samples to reduce overfitting. However, acquiring spoof data is very
expensive since the live faces should be re-printed and re-captured in many
views. In this paper, we present a method to synthesize virtual spoof data in
3D space to alleviate this problem. Specifically, we consider a printed photo
as a flat surface and mesh it into a 3D object, which is then randomly bent and
rotated in 3D space. Afterward, the transformed 3D photo is rendered through
perspective projection as a virtual sample. The synthetic virtual samples can
significantly boost the anti-spoofing performance when combined with a proposed
data balancing strategy. Our promising results open up new possibilities for
advancing face anti-spoofing using cheap and large-scale synthetic data.
