# Improving Face Anti-Spoofing by 3D Virtual Synthesis

Source: [Improving Face Anti-Spoofing by 3D Virtual Synthesis](<https://www.sota2.com/research/paper/68fa69481e209131cea21254>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-04T03:02:12.802Z

Authors: Jianzhu Guo, Xiangyu Zhu, Jinchuan Xiao, Zhen Lei, Genxun Wan, Stan Z. Li

Published: 2019-01-02T18:40:33.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Face Anti-Spoofing on CASIA-RFS (test)](<https://www.sota2.com/research/sota/face-anti-spoofing-on-casia-rfs-test.md>): Face Anti-Spoofing; CASIA-RFS (test); APCER: 4.68.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1901.00488>)

[PDF](<https://arxiv.org/pdf/1901.00488.pdf>)