计算机科学 ›› 2020, Vol. 47 ›› Issue (2): 112-117.doi: 10.11896/jsjkx.181202339

• 计算机图形学&多媒体 • 上一篇    下一篇

基于图像扩散速度模型和纹理信息的人脸活体检测

李新豆,高陈强,周风顺,韩慧,汤林   

  1. (重庆邮电大学通信与信息工程学院信号与信息处理重庆市重点实验室 重庆400065)
  • 收稿日期:2018-12-17 出版日期:2020-02-15 发布日期:2020-03-18
  • 通讯作者: 高陈强(gaocq@cqupt.edu.cn)
  • 基金资助:
    国家自然科学基金(61571071);重庆市科委自然科学基金(cstc2014jcyjA40048)

Face Liveness Detection Based on Image Diffusion Speed Model and Texture Information

LI Xin-dou,GAO Chen-qiang,ZHOU Feng-shun,HAN Hui,TANG Lin   

  1. (Chongqing Key Laboratory of Signal and Information Processing,College of Information and Communication Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)
  • Received:2018-12-17 Online:2020-02-15 Published:2020-03-18
  • About author:LI Xin-dou,born in 1992,postgraduate.His main research interests indude face liveness detection;GAO Chen-qiang,born in 1981,Ph.D,professor.His research interests include image processing,infrared target detection and event detection.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61571071) and Natural Science Foundation of Chongqing Science and Technology Commission (cstc2014jcyjA40048).

摘要: 为了解决人脸身份认证中的欺诈问题,提出了一种基于图像扩散速度模型和纹理信息的人脸活体检测算法。真实人脸和虚假人脸图像的空间结构不同,为了提取这种差异特征,该方法使用各向异性扩散增强图像的边缘信息。然后,将原始图像与扩散后图像的差值作为图像的扩散速度,并构建扩散速度模型。接着使用局部二值算法提取图像扩散速度特征并训练分类器。真实人脸图像和虚假人脸图像之间存在很多差异特征,为了进一步提高人脸活体检测算法的泛化能力,该方法同时提取人脸图像的模糊程度特征和色彩纹理特征,通过特征矩阵级联的方法将两种特征进行融合,并训练另一个分类器。最后根据分类器输出概率加权融合的结果做出判决。实验结果表明,该算法能够快速有效地检测出虚假的人脸图像。

关键词: 各向异性扩散, 活体检测, 局部区域二值, 人脸识别

Abstract: To solve the problem of fraud in face authentication,this paper proposed a face liveness detection algorithm based on image diffusion speed model and texture information.The spatial structures of real face and fake face images are different.In order to extract difference features,anisotropic diffusion is used to enhance image edge information.And then,the difference between the original image and the diffused image is used as the image diffusion speed,and a diffusion velocity model is contructed.Then,local binary pattern algorithm is used to extract the diffusion speed feature and train a classifier.There are many differences between real face images and fake face images.In order to further improve the generalization ability of face liveness detection,the blur degree and color feature of face image are extracted synchronously.These features are combined by cascading feature matrix and another classifier is trained.Finally,a judgment is made based on the probabilities weighted fusion result of classifier output.Experimental results show that the proposed algorithm can detect spoofing faces quickly and efficiently.

Key words: Anisotropic diffusion, Face liveness detection, Face recognition, Local binary pattern

中图分类号: 

  • TP391.41
[1]BOULKENAFET Z,KOMULAINEN J,HADID A.Face anti-spoofing based on color texture analysis[C]∥IEEE Internatio-nal Conference on Image Processing.Quebec,Canada:IEEE,2015:2636-2640.
[2]ZHANG Z,YI D,LEI Z,et al.Face liveness detection by lear-ning multispectral reflectance distributions[C]∥Proceedings of IEEE International Conference on Automatic Face & Gesture Recognition and Workshops.Santa Barbara CA,USA:IEEE Computer Society Press,2011:436-441.
[3]MOHAN K,CHANDRASEKHAR P,JILANI S A K.A Combined HOG-LPQ with Fuz-SVM Classifier for Object Face Liveness Detection[C]∥International Conference on ISmac.Palla-dam,India:IEEE Computer Society Press,2017:531-537.
[4]MAATTA J,HADID A,PIETIKAINEN M.Face spoofing detection from single images using texture and local shape analysis[J].IET Biometrics,2012,1(1):3-10.
[5]PEREIRA T D F,ANJOS A,MARTINO J M D,et al.LBP-TOP,Based Countermeasure against Face Spoofing Attacks[C]∥Proceedings of International Conference on Computer Vision.Berlin,Heidelberg:Springer-Verlag,2012:121-132.
[6]LAKSHMINARAYANA N N,NARAYAN N,NAPP N,et al.A discriminative spatio-temporal mapping of face for liveness detection[C]∥IEEE International Conference on Identity,Security and Behavior Analysis.New Delhi,India:IEEE Computer Society Press,2017:1-7.
[7]TAN X,LI Y,LIU J,et al.Face liveness detec tion from a single image with sparse low rank bilinear discriminative model[C]∥Proceedings of European Conference on Computer Vision.Crete,Greece:Springer-Verlag,2010:504-517.
[8]ZHANG Z,YAN J,LIU S,et al.A face antis-poofing database with diverse attacks[C]∥Pro ceedings of Iapr International Conference on Biometrics.New Delhi,India:IEEE Computer Society Press,2012:26-31.
[9]WANG T,YANG J W,LEI Z,et al.Face liveness detection using 3D structure recovered from a single camera[C]∥Procee-dings of IEEE International Conference on Biometrics.Mdrid,Spain:IEEE Computer Society Press,2013:1-6.
[10]YANG J,LEI Z,LI S Z.Learn Convolutional Neural Network for Face Anti-Spoofing[J].Computer Science,2014,9218:373-384.
[11]AKBULUT Y,SENGUR A,BUDAK U,et al.Deep Learning based Face Liveness Detetion in Videos[C]∥Intertional Artificial Intelligence and Data Processing Symposium.Malatya,Turkey:IEEE Computer Society Press,2017.
[12]CAO Y,TU L,WU L F.Face Liveness Detection using Gray Level Co-Occur rence Matrix and Wavelets Analysis in Identity Authentication[J].Journal of Signal processing,2014(7):830-835.
[13]KIM W,SUH S,HAN J J.Face Liveness Detection from a Single Image via Diffusion Speed Model.[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2015,24(8):2456-2456.
[14]CHANG C C,LIN C J.LIBSVM:A libraryfor support vector machines[M].New York:Association for Computing Machinery,2011:1-27.
[15]PERONA P,MALIK J.Scale-Space and Edge Detection Using Anisotropic Diffusion[M].IEEE Computer Society,1990:629-639.
[16]WEICKERT J,ROMENY B H,VIERGEVER M A.Efficient and reliable schemes for nonlinear diffusion filtering[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,1998,7(3):398.
[17]WANG Z,BOVIK A C,SHEIKH H R,et al.Image quality assessment:from error visibility to structural similarity[J].IEEE Transactions on Image Processing,2004,13(4):600-612.
[18]WEN D,HAN H,JAIN A K.Face Spoof Detection With Image Distortion Analysis[J].IEEE Transactions on Information Forensics & Security,2015,10(4):746-761.
[19]CHINGOVSKA I,ANJOS A,MARCEL S.On the effectiveness of local binary patterns in face anti-spoofing[C]∥Proceedings of Biometrics Special Interest Group.Darmstadt,Germany:IEEE Computer Society Press,2012:1-7.
[20]LIU X,KAN M,WANGLONG W U,et al.VIPLFaceNet:an Open Source Deep Face Recognition SDK[J].Frontiers of Computer Science,2017,11(2):208-218.
[21]YANG J,LEI Z,LIAO S,et al.Face liveness detection with component dependent descriptor[C]∥Proceedings of International Conference on Biometrics.Madrid,Spain:IEEE Computer Society Press,2013:1-6.
[22]ALOTAIBI A,MAHMOOD A.Deep face liveness detection based on nonlinear diffusion using convolution neural network[J].Signal Image & Video Processing,2016,11(4):1-8.
[23]XU Z,LI S,DENG W.Learning temporal fea tures using LSTM-CNN architecture for face anti-spoofing[C]∥Procee-dings of Pattern Recognition.Kuala Lumpur,Malaysia:IEEE Computer Society Press,2016:141-145.
[1] 黄璞, 杜旭然, 沈阳阳, 杨章静.
基于局部正则二次线性重构表示的人脸识别
Face Recognition Based on Locality Regularized Double Linear Reconstruction Representation
计算机科学, 2022, 49(6A): 407-411. https://doi.org/10.11896/jsjkx.210700018
[2] 黄璞, 沈阳阳, 杜旭然, 杨章静.
基于局部约束特征线表示的人脸识别
Face Recognition Based on Locality Constrained Feature Line Representation
计算机科学, 2022, 49(6A): 429-433. https://doi.org/10.11896/jsjkx.210300169
[3] 程祥鸣, 邓春华.
基于无标签知识蒸馏的人脸识别模型的压缩算法
Compression Algorithm of Face Recognition Model Based on Unlabeled Knowledge Distillation
计算机科学, 2022, 49(6): 245-253. https://doi.org/10.11896/jsjkx.210400023
[4] 魏勤, 李瑛娇, 娄平, 严俊伟, 胡辑伟.
基于边云协同的人脸识别方法研究
Face Recognition Method Based on Edge-Cloud Collaboration
计算机科学, 2022, 49(5): 71-77. https://doi.org/10.11896/jsjkx.210300222
[5] 何嘉玉, 黄宏博, 张红艳, 孙牧野, 刘亚辉, 周哲海.
基于深度学习的单幅图像三维人脸重建研究综述
Review of 3D Face Reconstruction Based on Single Image
计算机科学, 2022, 49(2): 40-50. https://doi.org/10.11896/jsjkx.210500215
[6] 陈长伟, 周晓峰.
快速局部协同表示分类器及其在人脸识别中的应用
Fast Local Collaborative Representation Based Classifier and Its Applications in Face Recognition
计算机科学, 2021, 48(9): 208-215. https://doi.org/10.11896/jsjkx.200800155
[7] 温荷, 罗频捷.
基于改进脉冲耦合神经网络的动态人脸识别
Dynamic Face Recognition Based on Improved Pulse Coupled Neural Network
计算机科学, 2021, 48(6A): 85-88. https://doi.org/10.11896/jsjkx.200600172
[8] 吴晓丽, 胡伟.
基于注意力的热点块和显著像素卷积神经网络的人脸防伪方法
Attention-based Hot Block and Saliency Pixel Convolutional Neural Network Method for Face Anti-spoofing
计算机科学, 2021, 48(4): 316-324. https://doi.org/10.11896/jsjkx.200300128
[9] 白子轶, 毛懿荣, 王瑞平.
视频人脸识别进展综述
Survey on Video-based Face Recognition
计算机科学, 2021, 48(3): 50-59. https://doi.org/10.11896/jsjkx.210100210
[10] 杨章静, 王文博, 黄璞, 张凡龙, 王昕.
基于局部加权表示的线性回归分类器及人脸识别
Local Weighted Representation Based Linear Regression Classifier and Face Recognition
计算机科学, 2021, 48(11A): 351-359. https://doi.org/10.11896/jsjkx.210100173
[11] 栾晓, 李晓双.
基于多特征融合的人脸活体检测算法
Face Anti-spoofing Algorithm Based on Multi-feature Fusion
计算机科学, 2021, 48(11A): 409-415. https://doi.org/10.11896/jsjkx.210100181
[12] 陆要要, 袁家斌, 何珊, 王天星.
基于超分辨率重建的低质量视频人脸识别方法
Low-quality Video Face Recognition Method Based on Super-resolution Reconstruction
计算机科学, 2021, 48(11A): 295-302. https://doi.org/10.11896/jsjkx.201200159
[13] 吴庆洪, 高晓东.
稀疏表示和支持向量机相融合的非理想环境人脸识别
Face Recognition in Non-ideal Environment Based on Sparse Representation and Support Vector Machine
计算机科学, 2020, 47(6): 121-125. https://doi.org/10.11896/jsjkx.190500058
[14] 韩旭, 谌海云, 王溢, 许瑾.
基于SPCA和HOG的单样本人脸识别算法
Face Recognition Using SPCA and HOG with Single Training Image Per Person
计算机科学, 2019, 46(6A): 274-278.
[15] 金堃, 陈少昌.
步态识别现状与发展
Status and Development of Gait Recognition
计算机科学, 2019, 46(6A): 30-34.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!