Improving Face Anti-Spoofing by 3D Virtual Synthesis | |
Jianzhu Guo; Xiangyu Zhu; Jinchuan Xiao; Zhen Lei; Genxun Wan; Stan Z. Li | |
2019 | |
会议日期 | 2019/06/04-2019/06/07 |
会议地点 | Crete, Greece |
英文摘要 | 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. |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/25839] |
专题 | 生物识别与安全技术研究中心 |
作者单位 | 1.模式识别国家重点实验室 2.中国科学院大学 3.公安部第一研究所 |
推荐引用方式 GB/T 7714 | Jianzhu Guo,Xiangyu Zhu,Jinchuan Xiao,et al. Improving Face Anti-Spoofing by 3D Virtual Synthesis[C]. 见:. Crete, Greece. 2019/06/04-2019/06/07. |
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