人脸活体检测综述  

Review on face liveness detection

作　　者：谢晓华[1,2] 卞锦堂 赖剑煌 Xie Xiaohua;Bian Jintang;Lai Jianhuang(School of Computer Science and Engineering,Sun Yat-sen University,Guangzhou 510006,China;Guangdong Province Key Laboratory of Information Security Technology,Guangzhou 510006,China)

机构地区：[1]中山大学计算机学院,广州510006 [2]广东省信息安全重点实验室,广州510006

出　　处：《中国图象图形学报》2022年第1期63-87,共25页Journal of Image and Graphics

基　　金：国家自然科学基金项目(62072482)。

摘　　要：人脸识别系统往往面临着各类人脸欺诈攻击,如打印相片、屏幕播放和3维面具等。如何区分真实人脸与虚假人脸,亦称人脸活体检测,对于人脸识别系统的安全具有十分重要的意义。近年来,已有大量人脸活体检测方法相继提出,部分已经成功获得实际应用。本文对人脸活体检测技术进行了全面的梳理回顾,包括硬件方案、算法、数据集、技术标准以及业界实际应用情况。最后,进行了总结与展望。整体而言,基于多模态数据,采取先验知识启发的深度学习方法目前能获得占优的人脸活体验证精度。随着人脸欺诈攻击方式的不断升级变更,面向未知类型攻击的人脸活体检测研究愈加重要,此外,新型的传感硬件方案也值得鼓励探讨。Face recognition technology has been widely used nowadays,such as smart phone unlocking,users’account verification,access control system,financial payment,public security pursuit,and so forth.Face recognition system has been challenging with various face fraud attacks,such as printing photos,screen playing,3D masks,etc.Bending the printed face to make it have a general three-dimensional structure of the face in practice.Meanwhile,the movement of the key components of the real face can be integrated into the fake face via fake coverage of the printed face with the hollowedout eyes and mouth.Face spoofing technologies has aimed to present realistic apparent texture,accurate three-dimensional face structure,reasonable face motion,and discriminative target identity features generally.The issue of false face distinguishing,also known as face liveness detection,is of great significance to the security of face recognition systems.This research reviewed current face liveness detection technologies based on hardware,algorithm,data set,technical standards,and practical application.For hardware,some popular tools used for face liveness detection,such as RGB cameras,binocular cameras,(near)infrared cameras,depth cameras,three-dimensional scanners,light field cameras and multispectral imagers.Flash lamps are used for assistance as well.For algorithms,the original methods distinguish a real face and a fake face via analyzing the texture information,motion information,image quality,structure information,and three-dimensional shape in the video or image.The analysis is assisted based on user interaction or changing the environment with flashing.Deep learning technology has been using in face liveness detection as well.It is necessary to opt the appropriate face detec tion method in accordance with the targeted application scenario.In reality,face liveness detection technologies are mainly used in unsupervised authentication scenarios,such as smartphone unlocking,mobile app login,the self-service terminal of the bank,and attend

关 键 词：人脸活体验证 人脸防伪 人脸识别 深度学习 特征学习 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]