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收稿：2021-06-29，
修回：2021-9-27，
录用：2021-10-4，
纸质出版：2022-01-16
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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 hollowed-out 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 detection 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 attendance machine. The illustrated methods are mainly evolved as following: 1) interactive verification based on a single camera
that is
the target is required to perform cooperative actions
such as shaking the head
blinking
opening mouth
and reading Arabic numerals; 2) the face liveness detection based on "visible light + near-infrared" dual-mode camera
in which near-infrared image is conducive to distinguish skin from other materials; 3) the face liveness detection based on "visible light + depth" dual-mode camera
in which the depth camera used to obtain the three-dimensional information of the object. The two methods latter can extract rich features for face liveness detection based on hardware combinations. Overall
based on multimodal data
the deep learning method has been adopted to obtain the optimal accuracy of liveness verification based on prior knowledge.In early 2018
China Information Security Standardization Technical Committee established the standards of liveness detection technology in the face recognition systems. In 2020
IEEE released the first international standard for face liveness detection (IEEE Std 2790-2020). With the emergence of new fraud methods
the research of face liveness detection will also encounter new challenges. First
face spoofing and face liveness detection will promote and upgrade each other. Therefore
it is particularly important to study the detection of unknown types of face fraud attacks. For unknown types of attacks
a feasible solution is to detect invisible attacks as abnormal samples. Next
the of face fraud upgrading means is related to the update of fraud media. In particular
the progress of high-precision 3D printing
flexible screen
and physiological mask will make face fraud more difficult to detect. Face liveness detection also needs to be developed at the hardware level consistently. It is necessary to explore the application of advanced sensing equipment such as multi-spectrometer
light field camera
and even ultrasound in face liveness detection. Moreover
the deep learning method plays a leading role in obtaining high accuracy. The lack of interpretability is also a major criticism of deep learning. For the application of face liveness detection
the interpretation of the deep learning methods needs to be conducted further. The interpretability mechanism has its priority to the design of the learning model
and to the integration of prior knowledge into the learning model. At last
it is necessary to develop a unified framework for joint face recognition and face liveness detection
which can improve the accuracy of both simultaneously.
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