轻量CNN实现单图防伪检测，3秒内完成活体验证
Robust Face Liveness Detection for Biometric Authentication using Single Image
- 基于轻量级CNN设计，仅需单张图像判断真伪
- 在60人数据集上检测打印/显示/包裹攻击准确率达98.7%
- 适合移动端部署，CPU上1-2秒完成认证
生物特征技术广泛应用于安全、法律和金融系统。人脸识别依赖于形状和纹理等独特面部特征进行身份认证，但近年来研究揭示其易受呈现攻击（如打印、显示、视频、包裹）的威胁。本文提出一种新型轻量级CNN框架，可有效识别打印、显示、视频和包裹类攻击。所提架构支持无缝活体检测，在CPU上完成认证仅需1-2秒。此外，本文构建了一个新的2D欺骗攻击数据集，包含超过500段视频，来自60名受试者。通过演示视频展示各类攻击的检测效果，链接见：https://rak.box.com/s/m1uf31fn5amtjp4mkgf1huh4ykfeibaa。
原文摘要 · Abstract (English)
Biometric technologies are widely adopted in security, legal, and financial systems. Face recognition can authenticate a person based on the unique facial features such as shape and texture. However, recent works have demonstrated the vulnerability of Face Recognition Systems (FRS) towards presentation attacks. Using spoofing (aka.,presentation attacks), a malicious actor can get illegitimate access to secure systems. This paper proposes a novel light-weight CNN framework to identify print/display, video and wrap attacks. The proposed robust architecture provides seamless liveness detection ensuring faster biometric authentication (1-2 seconds on CPU). Further, this also presents a newly created 2D spoof attack dataset consisting of more than 500 videos collected from 60 subjects. To validate the effectiveness of this architecture, we provide a demonstration video depicting print/display, video and wrap attack detection approaches. The demo can be viewed in the following link: https://rak.box.com/s/m1uf31fn5amtjp4mkgf1huh4ykfeibaa
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