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人脸活体检测综述

蒋方玲 刘鹏程 周祥东

蒋方玲, 刘鹏程, 周祥东. 人脸活体检测综述. 自动化学报, 2021, 47(8): 1799-1821 doi: 10.16383/j.aas.c180829
引用本文: 蒋方玲, 刘鹏程, 周祥东. 人脸活体检测综述. 自动化学报, 2021, 47(8): 1799-1821 doi: 10.16383/j.aas.c180829
Jiang Fang-Ling, Liu Peng-Cheng, Zhou Xiang-Dong. A review on face anti-spoofing. Acta Automatica Sinica, 2021, 47(8): 1799-1821 doi: 10.16383/j.aas.c180829
Citation: Jiang Fang-Ling, Liu Peng-Cheng, Zhou Xiang-Dong. A review on face anti-spoofing. Acta Automatica Sinica, 2021, 47(8): 1799-1821 doi: 10.16383/j.aas.c180829

人脸活体检测综述

doi: 10.16383/j.aas.c180829
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

  • 本文责任编委 刘青山

A Review on Face Anti-spoofing

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
  • Recommended by Associate Editor LIU Qing-Shan
  • 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.
    Recommended by Associate Editor LIU Qing-Shan
    1)  本文责任编委 刘青山
  • 图  1  传统人脸识别技术的安全性缺陷

    Fig.  1  Vulnerability of conventional face recognition system

    图  2  不同类别假体人脸示例

    Fig.  2  Examples of spoofing faces

    图  3  Replay-Attack数据集中的假体人脸

    Fig.  3  Spoofing faces of Replay-Attack

    图  4  人脸活体检测方法分类

    Fig.  4  Classification of face anti-spoofing methods

    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

    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
    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
    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
    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[8485, 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]
    下载: 导出CSV

    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
    下载: 导出CSV

    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
    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
    下载: 导出CSV

    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
    下载: 导出CSV

    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
    下载: 导出CSV

    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
    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
    下载: 导出CSV

    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
    下载: 导出CSV

    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
    下载: 导出CSV
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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

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