Improving Face Anti-Spoofing by 3D Virtual Synthesis

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...

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Bibliographic Details
Published inarXiv.org
Main Authors Guo, Jianzhu, Zhu, Xiangyu, Xiao, Jinchuan, Lei, Zhen, Wan, Genxun, Li, Stan Z
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 10.02.2021
Subjects
Data acquisition
Face recognition
Flat surfaces
Machine learning
Sampling methods
Spoofing
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Summary: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.
ISSN:2331-8422