面向VTM的交互式活体检测算法
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摘要
为提升远程视频柜员机人脸识别登录系统的识别率和安全性,将改进的眨眼检测、背景检测和随机组合动作指令相结合,提出一种交互式活体检测算法。基于OpenCV级联分类器人脸检测和局部二值特征人脸对齐算法,结合坐标比例和眼球色素变化改进眨眼检测。利用背景检测和随机组合动作指令抵御动态视频攻击,加入图像质量检测与校正功能,使系统在弱光、歪斜等环境影响下对活体人脸检测有较好的检测效果。在活体人脸数据库CASIA-FASD和自建样本库上进行实验,结果表明,该算法识别率达到97.67%,与多光谱、卷积神经网络等检测算法相比性能有明显的提升。
In order to improve the recognition rate and security of the Video Teller Machine(VTM) face recognition login system,an interactive liveness detection algorithm that combines improved blink detection,background detection and random combined action instructions is proposed.Based on the OpenCV cascade classifier face detection and Local Binary Feature(LBF) face alignment algorithm,combining the coordinate proportion and the eye pigment change,the detection method is improved.Uses the background detection and the random combination action instruction to resist the dynamic video attack.Making use of the image quality detection and correction function,the system in weak light,skew and the other environmental condition has a good performance as well.Experiments are carried out on liveness face database CASIA-FASD and self-built sample library,the result shows that the recognition rate reaches 97.67%,which is obviously improved than multispectral,convolutional neural network,and the other existing detection algorithms.
In order to improve the recognition rate and security of the Video Teller Machine(VTM) face recognition login system,an interactive liveness detection algorithm that combines improved blink detection,background detection and random combined action instructions is proposed.Based on the OpenCV cascade classifier face detection and Local Binary Feature(LBF) face alignment algorithm,combining the coordinate proportion and the eye pigment change,the detection method is improved.Uses the background detection and the random combination action instruction to resist the dynamic video attack.Making use of the image quality detection and correction function,the system in weak light,skew and the other environmental condition has a good performance as well.Experiments are carried out on liveness face database CASIA-FASD and self-built sample library,the result shows that the recognition rate reaches 97.67%,which is obviously improved than multispectral,convolutional neural network,and the other existing detection algorithms.
引文
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[2] WU L,XU Y,XU X,et al.A face liveness detection scheme to combining static and dynamic features[M].Berlin,Germany:Springer,2016:628-636.
[3] MOHAN K,CHANDRASEKHAR P,JILANI S A K.A combined HOG-LPQ with Fuz-SVM classifier for object face liveness detection[C]//Proceedings of International Conference on IoT in Social,Mobile,Analytics and Cloud.Washington D.C.,USA:IEEE Press,2017:531-537.
[4] YEH C H,CHANG H H.Face liveness detection with feature discrimination between sharpness and blurriness[C]//Proceedings of the 15th IAPR International Conference on Machine Vision Applications.Washington D.C.,USA:IEEE Press,2017:398-401.
[5] SINGH M,ARORA A S.A robust anti-spoofing technique for face liveness detection with morphological operations[J].Optik-International Journal for Light and Electron Optics,2017,139(4):347-354.
[6] LAKSHMINARAYANA N N,NARAYAN N,NAPP N,et al.A discriminative spatio-temporal mapping of face for liveness detection[C]//Proceedings of IEEE International Conference on Identity,Security and Behavior Analysis.Washington D.C.,USA:IEEE Press,2017:1-7.
[7] 任玉强,田国栋,周祥东,等.高安全性人脸识别系统中的唇语识别算法研究[J].计算机应用研究,2017,34(4):1221-1225.
[8] WANG Y,HAO X,HOU Y,et al.A new multispectral method for face liveness detection[C]//Proceedings of IAPR Asian Conference on Pattern Recognition.Washington D.C.,USA:IEEE Press,2013:922-926.
[9] BOULKENAFET Z,KOMULAINEN J,HADID A.Face spoofing detection using colour texture analysis[J].IEEE Transactions on Information Forensics and Security,2016,11(8):1818-1830.
[10] RAGHAVENDRA R,RAJA K B,VENKATESH S,et al.On the vulnerability of extended multispectral face recognition systems towards presentation attacks[C]//Proceedings of IEEE International Conference on Identity,Security and Behavior Analysis.Washington D.C.,USA:IEEE Press,2017:1-8.
[11] PAN G,SUN L,WU Z,et al.Eyeblink-based anti-spoofing in face recognition from a generic webcamera[C]//Proceedings of International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2007:1-8.
[12] FENG L,PO L M,LI Y,et al.Face liveness detection using shearlet-based feature descriptors[EB/OL].[2018-01-05].http://www.ee.cityu.edu.hk/～lmpo/publications/2017_JEI_face_liveness.pdf.
[13] 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.
[14] 曹瑜,涂玲,毋立芳.身份认证中灰度共生矩阵和小波分析的活体人脸检测算法[J].信号处理,2014,30(7):830-835.
[15] MEI L,YANG D,FENG Z,et al.WLD-TOP based algorithm against face spoofing attacks[M].Berlin,Germany:Springer,2015:6.
[16] WILD P,RADU P,CHEN L,et al.Robust multimodal face and fingerprint fusion in the presence of spoofing attacks[J].Pattern Recognition,2016,50(C):17-25.
[17] NOBLE F K.Comparison of OpenCV’s feature detectors and feature matchers[C]//Proceedings of International Conference on Mechatronics and Machine Vision in Practice.Washington D.C.,USA:IEEE Press,2016:1-6.
[18] REN S,CAO X,WEI Y,et al.Face alignment at 3 000 FPS via regressing local binary features[C]//Proceedings of International Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2014:1685-1692.
[19] ZHANG Z,YAN J,LIU S,et al.A face anti-spoofing database with diverse attacks[C]//Proceedings of the 5th IAPR International Conference on Biometrics.Washington D.C.,USA:IEEE Press,2012:26-31.
[20] LIAO S,JAIN A K,LI S Z.A fast and accurate unconstrained face detector[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2016,38(2):211-223.
[21] AKBULUT Y,SENGUR A,BUDAK U,et al.Deep learning based face liveness detection in videos[C]//Proceedings of International Artificial Intelligence and Data Processing Symposium.Washington D.C.,USA:IEEE Press,2017:1-4.