Improving Face Anti-Spoofing by 3D Virtual Synthesis

2 Jan 2019  ·  Jianzhu Guo, Xiangyu Zhu, Jinchuan Xiao, Zhen Lei, Genxun Wan, Stan Z. Li ·

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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Face Anti-Spoofing CASIA-MFSD 3D Synthesis (balancing sampling) EER 2.22 # 1
HTER 1.67 # 1
Face Anti-Spoofing Replay-Attack 3D Synthesis (balancing sampling) EER 0.25 # 2
HTER 0.63 # 2


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