人脸活体检测综述

蒋方玲; 刘鹏程; 周祥东

doi:10.16383/j.aas.c180829

人脸活体检测综述

doi: 10.16383/j.aas.c180829

蒋方玲^{1, 2,},
刘鹏程^1,,
周祥东^1, ,

1.
中国科学院重庆绿色智能技术研究院重庆 400714
2.
中国科学院大学北京 100049

基金项目:

国家重点研发计划 2018YFC0808300

国家自然科学基金 61806185

国家自然科学基金 61802361

国家自然科学基金 61602433

详细信息

作者简介:
蒋方玲中国科学院重庆绿色智能技术研究院博士研究生. 2012年获得天津大学计算机科学与技术专业硕士学位. 主要研究方向为人脸活体检测, 计算机视觉与模式识别.E-mail: jiangfangling@cigit.ac.cn

刘鹏程中国科学院重庆绿色智能技术研究院助理研究员. 2016年获得中国科学院自动化研究所工学博士学位. 主要研究方向为人脸识别, 跨领域图像识别. E-mail: liupengcheng@cigit.ac.cn

通讯作者:
周祥东中国科学院重庆绿色智能技术研究院副研究员. 2009年获得中国科学院自动化研究所工学博士学位. 主要研究方向为文字识别, 文档分析, 人脸识别. 本文通信作者.E-mail: zhouxiangdong@cigit.ac.cn

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出版历程
- 收稿日期: 2018-12-12
- 录用日期: 2019-04-19
- 刊出日期: 2021-08-20

A Review on Face Anti-spoofing

1.
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714
2.
University of Chinese Academy of Sciences, Beijing 100049

Funds:

National Key Research and Development Program of China 2018YFC0808300

National Natural Science Foundation of China 61806185

National Natural Science Foundation of China 61802361

National Natural Science Foundation of China 61602433

More Information

Author Bio:
JIANG Fang-Ling Ph. D. candidate at Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences. She received her master degree from Tianjin University in 2012. Her research interest covers face anti-spoofing, computer vision, and pattern recognition

LIU Peng-Cheng Research assistant at Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences. He received his Ph. D. degree from the Institute of Automation, Chinese Academy of Sciences in 2016. His research interest covers face recognition and cross-domain image recognition

Corresponding author: ZHOU Xiang-Dong Associate professor at Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences. He received his Ph. D. degree from the Institute of Automation, Chinese Academy of Sciences in 2009. His research interest covers handwriting recognition, ink document analysis, and face recognition. Corresponding author of this paper

摘要

摘要: 人脸活体检测是为了提高人脸识别系统安全性而需要重点研究的问题.本文首先从人脸活体检测的问题出发, 分个体、类内、类间三个层面对人脸活体检测存在的困难与挑战进行了阐述分析.接下来, 本文以算法使用的分类线索为主线, 分类别对人脸活体检测算法及其优缺点进行了梳理和总结.之后, 本文就常用人脸活体检测数据集的特点、数据量、数据多样性等方面进行了对比分析, 对算法评估常用的性能评价指标进行了阐述, 总结分析了代表性人脸活体检测方法在照片视频类数据集CASIA-MFSD、Replay-Attack、Oulu-NPU、SiW以及面具类数据集3DMAD、SMAD、HKBU-MARsV2上的实验性能.最后本文对人脸活体检测未来可能的发展方向进行了思考和探讨.
- 人脸活体检测 /
- 计算机视觉 /
- 人脸识别 /
- 深度学习 /
- 特征表达
Abstract: Face anti-spoofing is an important research field for ensuring the security of face recognition system. In this paper, we first discuss the difficulties and challenges in the development of face anti-spoofing. Then we take the classification clues utilized by the methods as the main line to review the achievements in the study of face anti-spoofing. Next, we analyze the characteristics, data volume and data diversity of commonly used face anti-spoofing datasets. The evaluation metrics of performances commonly used in algorithm evaluation are expounded. We summarize and analyze the performances of representative face anti-spoofing methods on CASIA-MFSD, Replay-Attack, Oulu-NPU, SiW, 3DMAD, SMAD and HKBU-MARsV2 datasets. Finally, we discuss the future development direction of face anti-spoofing.
- Face anti-spoofing /
- computer vision /
- face recognition /
- deep learning /
- feature representations
Recommended by Associate Editor LIU Qing-Shan
注释:

1) 本文责任编委刘青山

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图 1 传统人脸识别技术的安全性缺陷

Fig. 1 Vulnerability of conventional face recognition system

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图 2 不同类别假体人脸示例

Fig. 2 Examples of spoofing faces

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图 3 Replay-Attack数据集中的假体人脸

Fig. 3 Spoofing faces of Replay-Attack

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图 4 人脸活体检测方法分类

Fig. 4 Classification of face anti-spoofing methods

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图 5 各类人脸活体检测方法性能分布图

Fig. 5 Performance comparison of different category of face anti-spoofing methods

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表 1 主流人脸活体检测方法总览

Table 1 Brief overview of face anti-spoofing methods

一级类别	二级类别	分类特征	防范的假体人脸	算法优点	算法缺点
交互式人脸活体检测	基于随机动作的方法	用户配合的动作: 点头、抬头、眨眼、闭眼、遮挡眼睛、扬眉、皱眉、笑脸、吐舌头、张嘴^{[8, 12]}	照片、视频	对二维类假体人脸准确率高, 通用性强	需要用户配合, 用户体验差, 不能防止眼部、嘴部挖洞的面具攻击, 适用范围窄
交互式人脸活体检测	基于唇语声音混合的方法	朗读一个数字串、一段文字时的唇语与声音^[10-11]
非交互式人脸活体检测	基于图像纹理的方法	LBP、HOG、Gabor等描述符从灰度图中抽取的灰度纹理特征^{[15-17, 21, 23, 30-33]}	照片、视频、面具	容易实现, 计算量少, 单张图片可预测结果, 速度快	容易被拍摄设备、光照条件、图像质量影响, 跨数据集通用能力不强
	基于图像纹理的方法	LBP、LPQ等描述符从HSV, YCbCr颜色空间图像中抽取的颜色纹理特征^{[20, 35, 39]}
	基于图像质量的方法	手工设计特征抽取图像镜面反射、颜色分布、清晰度方面的图像质量特征^[43-45]	照片、视频	针对单类假体人脸的跨数据集通用能力相对强, 速度快	需要根据假体人脸的类别设计具体特征, 跨假体类型的通用能力不强, 需要高质量图像, 难以抵御高清哑光照片视频攻击
	基于生命信息的方法	光流法、运动成分分解检测活体不自主地眨脸部、唇部的微运动^{[48, 51-52, 56]}	照片	对照片类假体人脸准确率高, 通用性高	需要视频为输入计算量大, 速度慢难以防范视频攻击对假体制造的微运动鲁棒性不强
	基于生命信息的方法	远程光学体积描记术(rPPG)信息检测待测	面具	特定约束条件下准确率高	需要视频为输入鲁棒性不强, 受外界光照、个体运动的影响大
	基于其他硬件的方法	近红外图像特征^[62-68]	照片、视频、面具	准确率高	需要增加新的昂贵硬件设备采集、处理图像的时间增加
		短波红外图像特征^[69]
		热红外图像特征^[70] 400 nm至1 000 nm的多个波段图像特征^[71-72] 光场图像信息^[73-74] 深度图像信息^[75-78]
	基于深度特征的方法	从头训练CNN抽取深度特征分类^{[79-80, 83]}、利用预训练的ResNet-50、VGG等模型抽取特征^{[84−85, 87]}	照片、视频、面具	相对来说, 准确率较高	模型参数多, 计算量大, 训练时间长过拟合问题对数据量和数据丰富性上有高要求
		深度特征与手工特征融合^{[85, 95-97]}
		三维卷积抽取时空深度特征^[93-94]
	混合特征类方法	纹理信息和运动生命信息的混合^{[17-19, 25, 85, 93-94, 97, 99-101]}	照片、视频、面具	融合多特征的优点提升识别准确率和通用性	计算量、存储增大, 相对识别时间增长算法实现和维护的工作量增加
		纹理信息和人脸结构信息的混合^{[76, 80, 83, 98, 102]}
		人脸结构信息与运动生命信息的混合^[89]
		图像质量与运动生命信息的混合^[95]
		背景信息^[27]和其他特征的混合^{[79, 81, 84, 87, 93, 98, 100]}

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表 2 主流人脸活体检测数据集总览

Table 2 Brief overview of face anti-spoofing datasets

数据集	年份	假体人脸	个体数	数据量	姿态、表情、光照等录制场景	录制设备与图像分辨率
NUAA^[116]	2010	三种打印照片	15	12 641张图像	三个不同光照的外界环境	网络摄像头–可见光图像640 × 480像素
Yale Recaptured^[33]	2011	LCD屏显示的照片	10	2 560张图像	64种不同光照	Kodak C813 8.2与Samsung Omnia i900的摄像头–裁剪后的灰度图 64 × 64像素
Print-Attack Database^[117]	2011	手持照片、固定照片	50	200个视频	两种不同光照	可见光图像苹果笔记本内置摄像头– 320 × 240像素
CASIA MFSD^[34]	2012	弯曲照片、挖眼照片、视频	50	600个视频	室内光照	可见光图像使用时间长的USB摄像头– 640 × 480像素; 新USB摄像头– 480 × 640像素; Sony NEX-5摄像头– 1 920 × 1 080像素
Replay Attack^[16]	2012	手持或者固定的照片与视频	50	1 300个视频	两种不同光照	可见光图像苹果笔记本内置摄像头– 320 × 240像素
MSU MFSD^[45]	2015	高分辨率照片与视频	35	280个视频	一个场景	可见光图像 MacBook Air 13内置摄像头– 640 × 480像素 Google Nexus 5前置摄像头– 720 × 480像素
UVAD^[31]	2015	6种设备拍摄的人脸视频	404	17 076个视频	不同背景光照的室内室外场景	索尼摄像头–可见光图像1 366 × 768像素
REPLAY MOBILE^[118]	2016	高分辨率照片与视频	40	1 200个视频	五种不同光照	iPad Mini2 (iOS) 以及LG-G4 前置摄像头–可见光图像720 × 1 280像素
MSU USSA^[119]	2016	高分辨率照片与视频	1 000	9 000张	一个场景	可见光图像 Google Nexus 5前置摄像头– 1 280 × 960像素; 后置摄像头– 3 264 × 2 448像素
Oulu-NPU^[120]	2017	照片与视频	55	5 940个视频	三种不同光照场景	六种智能手机的前置摄像头–可见光图像1 920 × 1 080像素
SiW^[89]	2018	高低两种分辨率的照片, 弯曲照片与高分辨率视频	165	4 478个视频	活体人脸录制了距离、姿态、表情、光照差异	Canon EOST6, Logitech C920摄像头–可见光图像1 920 × 1 080像素
GUC LiFFAD^[21]	2015	激光、喷墨打印的照片, iPad显示的照片	80	4 826张图像	不同焦距的图像, 室内室外场景	光场相机
Msspoof^[121]	2016	可见光与近红外光谱的黑白照片	22	4 704张图片	7种不同的室内室外环境	uEye摄像头以及近红外滤波器可见光与近红外图像– 1 280 × 1 024像素
EMSPAD^[122]	2017	激光打印的照片, 喷墨打印的照片	50	10 500张图像	2个场景	多光谱摄像头7个波段的图像裁剪对齐后120 × 120像素
3DMAD^[37]	2013	定制三维人脸面具	17	76 500张图像	3种不同场景	Kinect深度摄像头–深度图 640 × 480像素; 可见光摄像头–可见光图像640 × 480像素
HKBU MARsV2^[123]	2016	两种三维人脸面具	12	1 008个视频	7种不同光照	可见光图像, 三种传统摄像头: Logitech C920网络摄像头– 1 280 × 720像素; 工业摄像头– 800 × 600像素; Canon EOS M3-1 280 × 720像素; 可见光图像, 4种移动设备摄像头: Nexus 5, iPhone6, Samsung S7, Sony Tablet S;
SMAD^[92]	2017	硅胶三维人脸面具	–	130个视频	不同光照, 不同录制背景环境	–
MLFP^[124]	2017	挖去眼部的二维照片, 乳胶三维人脸面具	10	1 350个视频	室内室外场景	Android智能手机–可见光图像1 280 × 720像素; FLIR ONE热像仪安卓版–热红外图像640 × 480像素; 微软Kinect –近红外图像424 × 512像素

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表 3 CASIA-MFSD与Replay-Attack数据集单数据集测试性能数据(%)

Table 3 The performance of intra-test on CASIA-MFSD and Replay-Attack datasets (%)

方法	CASIA-MFSD	Replay-Attack
方法	EER	EER	HTER
LBP^[16]	18.2	13.9	13.8
DoG^[34]	17.0	–	–
Motion Magn^[55]	14.4	0.0	1.25
IDA^[43]	32.4	–	15.2
LBP-TOP^[17]	10.0	7.9	7.6
CNN^[79]	7.4	6.1	2.1
DMD + LBP^[56]	21.8	5.3	3.8
IDA and motion^[95]	5.8	0.83	0.0
Colour LBP^[20]	2.1	0.4	2.8
VLBC^[127]	6.5	1.7	0.8
3D CNN^[94]	5.2	0.16	0.04
FD-ML-LPQ-FS^[41]	4.6	5.6	4.8
patch + depthCNN^[80]	2.7	0.8	0.7
SURF^[39]	2.8	0.1	2.2
PreDRS + LSTM^[84]	1.22	1.03	1.18
ST Mapping^[82]	1.1	0.78	0.80
DDGL^[92]	–	1.3	0.0
LiveNet^[88]	4.59	–	5.74
Color texture^[35]	4.6	1.2	4.2
DSGN^[90]	3.42	0.13	0.63
deep LBP^[85]	2.3	0.1	0.9
3D CNN + geneloss^[93]	1.4	0.3	1.2
SSD + SPMT^[98]	0.04	0.04	0.06

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表 4 Oulu数据集单数据集测试性能数据(%)

Table 4 The performance of intra-test on Oulu dataset (%)

协议	方法	APCER	BPCER	ACER
1	GRADIANTex^[128]	7.1	5.8	6.5
1	CPq^[128]	2.9	10.08	6.9
1	GRADIANT^[128]	1.3	12.5	6.9
1	Auxiliary^[89]	1.6	1.6	1.6
1	Noise Modeling^[129]	1.2	1.7	1.5
1	TDI^[130]	2.5	0.0	1.3
2	GRADIANT^[128]	3.1	1.9	2.5
2	GRADIANTex^[128]	6.9	2.5	4.7
2	MixedFASNet^[128]	9.7	2.5	6.1
2	Auxiliary^[89]	2.7	2.7	2.7
2	Noise Modeling^[129]	4.2	4.4	4.3
2	TDI^[130]	1.7	2.0	1.9
3	GRADIANT^[128]	2.6±3.9	5.0±5.3	3.8±2.4
3	GRADIANTex^[128]	2.4±2.8	5.6±4.3	4.0±1.9
3	MixedFASNet^[128]	5.3±6.7	7.8±5.5	6.5±4.6
3	Auxiliary^[89]	2.7±1.3	3.1±1.7	2.9±1.5
3	Noise Modeling^[129]	4.0±1.8	3.8±1.2	3.6±1.6
3	TDI^[130]	5.9±1.0	5.9±1.0	5.9±1.0
4	GRADIANT^[128]	5.0±4.5	15.0±7.1	10.0±5.0
4	GRADIANTex^[128]	27.5±24.2	3.3±4.1	15.4±11.8
4	Massy HNU^[128]	35.8±35.3	8.3±4.1	22.1±17.6
4	Auxiliary^[89]	9.3±5.6	10.4±6.0	9.5±6.0
4	Noise Modeling^[129]	5.1±6.3	6.1±5.1	5.6±5.7
4	TDI^[130]	14.0±3.4	4.1±3.4	9.2±3.4

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表 5 SiW数据集单数据集测试性能数据(%)

Table 5 The performance of intra-test on SiW dataset (%)

评价协议	方法	APCER	BPCER	ACER
1	Auxiliary^[89]	3.58	3.58	3.58
1	TDI^[130]	0.96	0.50	0.73
2	Auxiliary^[89]	0.57±0.69	0.57±0.69	0.57±0.69
2	TDI^[130]	0.08±0.14	0.21±0.14	0.15±0.14
3	Auxiliary^[89]	8.31±3.81	8.31±3.80	8.31±3.81
3	TDI^[130]	3.10±0.81	3.09±0.81	3.10±0.81

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表 6 3DMAD、SMAD与HKBU-MARsV2数据集单数据集测试性能数据(%)

Table 6 The performance of intra-test on 3DMAD, SMAD and HKBU-MARsV2 datasets (%)

方法	3DMAD	SMAD	HKBU-MARsV2
方法	HTER	HTER	EER	HTER
LBPs^[38]	0.1	20.8	22.5	24.0±25.6
deep and color^[37]	0.95	–	–	–
IDA motion^[95]	0	–	–	–
Color texcure^[20]	–	–	23.0	23.4±20.5
videolet agg^[101]	0	20.4	–	–
GrPPG^[57]	7.94	–	16.4	16.1±20.5
LBP-TOP^[92]	–	21.5	–	–
DBN^[92]	0.5	19.2	–	–
DDGL^[92]	0	13.1	–	–
CFrPPG^[60]	6.82±12.1	–	4.04	4.42±5.1

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表 7 CASIA-MFSD与Replay-Attack数据集间跨数据集测试性能数据HTER (%)

Table 7 The performance of inter-test between CASIA-MFSD and Replay-Attack (%)

训练	CASIA-MFSD	Replay-Attack
测试	Replay-Attack	CASIA-MFSD
LBP^[126]	55.9	57.6
Motion^[126]	50.2	47.9
Motion Magn^[55]	50.1	47.0
LBP-TOP^[126]	49.7	60.6
CNN^[79]	48.5	45.5
Color LBP^[20]	30.3	37.7
texture+Motion^[99]	12.4	31.6
FD-ML-LPQ-FS^[41]	50.25	42.59
ST Mapping^[82]	35.05	40.22
SURF^[39]	26.9	23.2
DDGL^[92]	22.8	27.4
Noise Modeling^[129]	28.5	41.1
DeepImg+rPPG^[89]	27.6	28.4
Domain Adapt^[91]	27.4	36.0
Color texture^[35]	9.6	39.2
LiveNet^[88]	8.39	19.12

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表 8 3DMAD与HKBU-MARsV2数据集间跨数据集测试性能数据HTER (%)

Table 8 The performance of inter-test between 3DMAD and HKBU-MARsV2 (%)

训练	3DMAD	HKBU-MARsV2
测试	HKBU-MARsV2	3DMAD
Color texcure^[20]	40.1±7.8	47.7±5.4
LBPs^[38]	53.0±3.6	32.8±11.5
pretrain CNN^[60]	50.0±0.0	50.0±0.0
GrPPG^[57]	24.3±7.1	15.7±6.8
CFrPPG^[60]	2.51±0.1	2.55±0.1

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