基于微调策略的多线索融合人脸活体检测
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摘要
为解决身份认证过程中可能会出现的打印攻击、视频重播攻击等安全问题,提出一种多线索融合人脸活体检测方法。利用金字塔LK光流追踪视频帧并将其进行剪切波变换,以获取图像质量特征,通过卷积神经网络对数据集进行网络微调,得到真假活体。在Print-attack数据库和CISIA数据库上进行实验,结果表明,与LFDNet方法相比,该方法具有较高的人脸活体检测准确率,可用于抵制欺骗攻击。
To solve the security problems such as print attacks and video replay attacks that may occur during the authentication process,a multi-cue fusion method for detecting human faces is proposed.The pyramid Lucas-Kanade(LK) optical flow is used to track the video frame,and it is subjected to shear wave transform to obtain image quality features.The Convolutional Neural Network(CNN) is used to fine tune the data set to obtain true and false liveness bodies.Experiments on the Print-attack database and the CISIA database show that compared with the LFDNet method,this method has higher face liveness detection accuracy and can be applied to spoofing attacks resistance.
To solve the security problems such as print attacks and video replay attacks that may occur during the authentication process,a multi-cue fusion method for detecting human faces is proposed.The pyramid Lucas-Kanade(LK) optical flow is used to track the video frame,and it is subjected to shear wave transform to obtain image quality features.The Convolutional Neural Network(CNN) is used to fine tune the data set to obtain true and false liveness bodies.Experiments on the Print-attack database and the CISIA database show that compared with the LFDNet method,this method has higher face liveness detection accuracy and can be applied to spoofing attacks resistance.
引文
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[2] KOLLREIDER K,FRONTHALER H,FARAJ M I,et al.Real-time face detection and motion analysis with application in “liveness” assessment[J].IEEE Transactions on Information Forensics and Security,2007,2(3):548-558
[3] BAO Wei,LI Hong,LI Nan,et al.A liveness detection method for face recognition based on optical flow field[C]//Proceedings of International Conference on Image Analysis and Signal Processing.Washington D.C.,USA:IEEE Press,2009:233-236.
[4] 任安虎,刘贝.基于Adaboost的人脸识别眨眼检测[J].计算机与数字工程,2016,44(3):521-524.
[5] SINGH A K,JOSHI P,NANDI G C.Face recognition with liveness detection using eye and mouth movement[C]//Proceedings of International Conference on Signal Propagation and Computer Technology.Washington D.C.,USA:IEEE Press,2014:592-597.
[6] ANJOS A,CHAKKA M M,MARCEL S.Motion-based counter-measures to photo attacks in face recognition[J].IET Biometrics,2014,3(3):147-158
[7] MAATTA J,HADID A,PIETIKAINEN M.Face spoofing detection from single images using micro-texture analysis[C]//Proceedings of International Joint Conference on Biometrics.Washington D.C.,USA:IEEE Press,2011:1-7.
[8] 方天红,陈庆虎,廖海斌,等.融合纹理与形状的人脸加权特征[J].武汉大学学报(信息科学版),2015,40(3):321-326,340.
[9] KIM Y,NA J,YOON S,et al.Masked fake face detection using radiance measurements[J].Journal of the Optical Society of America A-Optics Image Science and Vision,2009,26(4):760-766.
[10] ZHANG Zhiwei,YI Dong,LEI Zhen,et al.Face liveness detection by learning multispectral reflectance distributions[C]//Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition and Workshops.Washington D.C.,USA:IEEE Press,2011:436-441.
[11] EASLEY G,LABATE D,LIM W Q.Sparse directional image representations using the discrete shearlet transform[J].Applied and Computational Harmonic Analysis,2008,25(1):25-46.
[12] 许晓.基于深度学习的活体人脸检测算法研究[D].北京:北京工业大学,2016.
[13] KUTYNIOKG,LABATE D.Shearlets:multiscale analysis for multivariate data[M].[S.l.]:Birkh?user Basel,2012.
[14] CHINGOVSKA I,ANJOS A,MARCEL S.On the effectiveness of local binary patterns in face anti-spoofing[C]//Proceedings of International Conference of Biometrics Special Interest Group.Washington D.C.,USA:IEEE Press,2012:183-194.
[15] ZHANG Zhiwei,YAN Junjie,LIU Sifei,et al.A face antispoofing database with diverse attacks[C]//Proceedings of the 5th IAPR International Conference on Biometrics.Washington D.C.,USA:IEEE Press,2012:26-31.