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.
@article{arxiv.1901.00488,
title = {Improving Face Anti-Spoofing by 3D Virtual Synthesis},
author = {Jianzhu Guo and Xiangyu Zhu and Jinchuan Xiao and Zhen Lei and Genxun Wan and Stan Z. Li},
journal= {arXiv preprint arXiv:1901.00488},
year = {2021}
}