基于多特征融合的人脸活体检测算法

doi:10.11896/jsjkx.210100181

摘要/Abstract

摘要： 近年来,随着人脸识别系统的不断发展,各种假冒合法用户的欺骗手段不断出现。基于单一差异线索进行的活体检测,已经不能满足当前复杂环境下提高人脸活体检测方法性能的需求。基于此,文中提出多特征融合的方法,使用卷积神经网络从人脸图像的不同线索中学习多个特征来进行活体检测,深度图在空间上能够区分真假人脸之间的深度信息;光流图在时间上能够区分真假人脸之间的动态信息;残差噪声图根据真人脸的一次成像和假冒人脸的二次成像噪声成分的不同进行区分。文中融合3种特征,不仅利用空间、时间多维度线索弥补了单一线索的不足,同时也提高了模型的泛化能力。相比现有的方法,所提方法无论是在同一个数据库还是跨数据库的情况下,均有较好的实验结果。具体而言,所提方法在CASIA数据集、REPLAY-ATTACK数据集和NUAA数据集上的错误率分别为0.11%,0.06%和0.45%。

关键词: 多特征融合, 活体检测, 人脸识别

Abstract: In recent years,with the development of face recognition systems,various spoofing methods that impersonate legitimate users appear.Face anti-spoofing detection method based on a single clue no longer meets the requirements of current face recognition system under complex environment.Based on this,we propose to use a convolutional neural network to learn multi-feature from different clues of face images,and to fuse the depth map,the face optical flow map,and the residual noise map to perform liveness detection.The depth map can distinguish the depth information between real and fake faces in space,the optical flow map can distinguish the dynamic information between real and fake faces in time,the residual noise map is based on the one-time imaging of the real face and the fake face.The secondary imaging noise components are distinguished by different components,and the three features are merged to use space,time and multi-dimensional clues to make up for the shortcomings of a single clue,and also improve the generalization ability of the model.Compared with the existing methods,our method shows promising results both on the single database and cross-databases.Specifically,equal error rate (EER) of our method on databases of CASIA,REPLAY-ATTACK and NUAA can achieve 0.11%,0.06% and 0.45%,respectively.

Key words: Face recognition, Multi-feature fusion, Spoofing detection

中图分类号:

TP391.41

栾晓, 李晓双. 基于多特征融合的人脸活体检测算法[J]. 计算机科学, 2021, 48(11A): 409-415. https://doi.org/10.11896/jsjkx.210100181

LUAN Xiao, LI Xiao-shuang. Face Anti-spoofing Algorithm Based on Multi-feature Fusion[J]. Computer Science, 2021, 48(11A): 409-415. https://doi.org/10.11896/jsjkx.210100181

参考文献

[1]LI X X,LING R H.Overview of occlusion face recognition:from subspace regression to deep learning[J].Chinese Journal of Computers,2017,9(2):634-639.
[2]WU W F N,LING X.Blind color image quality assessment base on color characteristics[J].Computer Science,2017,44(6A):151-156.
[3]DENG X,WANG H C,ZHAO L J,et al.A review of the research methods of face recognition anti-spoofing detection[J].Application Research of Computers,2020,37(9):2579-2585.
[4]ZHONG R,WU H Y,HE Y.Fast face recognition algorithm based on local fusion feature and hierarchical incremental tree[J].Computer Science,2018,45(22):308-313.
[5]MA Y K,WU L F,JIAN M,et al.An adversarial sample generation algorithm for face anti-spoofing detection[J].Journal of Software,2019,30(2):469-480.
[6]DE FREITAS PEREIRA T,ANJOS A,DE MARTINO J M,et al.LBP- TOP based countermeasure against face spoofing attacks[C]//Asian Conference on Computer Vision.Springer,Berlin,Heidelberg,2012:121-132.
[7]WANG Y,NIAN F,LI T,et al.Robust face anti-spoofing with depth information[J].Journal of Visual Communication and Image Representation,2017,49:332-337.
[8]SINGH A K,JOSHI P,NANDI G C.Face recognition with liveness detection using eye and mouth movement-[C]//Proceedings of the 2014 International Conference on Signal Propagation and Computer Technology.Piscataway:IEEE,2014:592-597.
[9]BHATTACHARJEE S,MOHAMMADI A,MARCEL S.Spoofing deep face recognitionwith custom silicone masks[C]//Proceedings of the IEEE 9th International Conference on Biometrics:Theory,Applications and Systems.Piscataway:IEEE,2018:1-7.
[10]GALBALLY J,MARCEL S,FIERREZ J.Image quality assessment for fake biometric detection:application to iris,fingerprint,and face recognition[J].IEEE Transactions on Image Processing,2014,23(2):710-724.
[11]KOMULAINEN J,HADID A,PIETIKÄINEN M.Contextbased face anti-spoofing[C]//Proceedings of the IEEE 6th International Conference on Biometrics:Theory,Applications and Systems.Piscataway:IEEE,2013:1-8.
[12]YANG J,LEI Z,LI S Z.Learn convolutional neural network for face anti-spoofing[J].arXiv:1408.5601,2014.
[13]LUCENA O,JUNIOR A,MOIA V,et al.Transfer learning using convolutional neural networks for face anti-spoofing[C]//Proceedings of the 2017 International Conference Image Analysis and Recognition,LNCS 10317.Cham:Springer,2017:27-34.
[14]MANJANI I,TARIYAL S,VATSA M,et al.Detecting silicone mask-based presentation attack via deep dictionary learning[J].IEEE Transactions on Information Forensics and Security,2017,12(7):1713-1723.
[15]GAN J,LI S,ZHAI Y,et al.3D convolutional neural network based on face anti-spoofing[C]//Proceedings of the 2^nd International Conference on Multimedia and Image Processing.Piscataway:IEEE,2017:1-5.
[16]TRONCI R,MUNTONI D,FADDA G,et al.Fusion of multiple clues for photo-attack detection in face recognition systems[C]//Proceedings of the 2011 IEEE International Joint Conference on Biometrics.Piscataway:IEEE,2011:1-6.
[17]KOMULAINEN J,HADID A,PIETIKÄINEN M,et al.Complementary countermeasures for detecting scenic face spoofing attacks[C]//Proceedings of the 2013 IEEE International Conference on Biometrics.Piscataway:IEEE,2013:1-7.
[18]TANG Y,WANG X,JIA X,et al.Fusing multiple deep features for face anti-spoofing[C]//Proceedings of t-he 2018 Chinese Conference on Biometric Recognition,LNCS 10996.Cham:Springer,2018:321-330.
[19]ATOUMY,LIU Y,JOURABLOO A,et al.Face anti-spoofing using patch and depth-based CNNs[C]//2017 IEEE International Joint Conference on Biometrics.IEEE,2017:319-328.
[20]BOULKENAFET Z,KOMULAINEN J,HADID A.Face spoofing detection using color texture analysis[J].IEEETransactions on Information Forensics and Security,2016,11(8):1818-1830.
[21]FENG L,PO L M,LI Y,et al.Integration of image quality and motion cues for face anti-spoofing:A neural net-work approach[J].Journal of Visual Communication and Image Representation,2016,38:451-460.
[22]SMIATACZ M.Liveness Measurements Using optical flow for biometric person authentication[J].Metrology and Measurement Systems,2012,19(2):257-268.
[23]JOURABLOO A,LIU Y J,LIU X M.Face de-spoofing:anti-spoofing via noise modeling[C]//European Conference on Computer Vision.2018:6-7.
[24]PARKIN A,GRINCHUK O.Recognizing multi-m-odal facespoofing with face recognition networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.2019:1617-1623.
[25]HE K M,ZHANG X Y,REN S Q,et al.Deep residual learning for image recognition[C]//Proc of The IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE Press,2016:770-778.
[26]HU J,SHEN L,SUN G.Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:7132-7141.
[27]ZHANG S,WANG X,LIU A,et al.A dataset and benchmark for large-scale multi-modal face anti-spoofing[C]//Proceedings of the IEEE Conference on Computer Visionand Pattern Recognition.2019:919-928.
[28]GARCIA D C,DE QUEIROZ R L.Face-spoofing 2D-detection based on moire-pattern analysis[J].IEEE Transactions on Information Forensics and Security,2015,10(4):778-786.
[29]FENG Y,WU F,SHAO X,et al.Joint 3d Facereconstruction and dense alignment wit.position map regression network[C]//European Conference on Computer Vision.2018:557-574.
[30]BAKER S,MATTHEWS I.Lucas-Kanade 20 years on:A unifying framework[J].International Journal of Computer Vision,2004,56(3):221-255.
[31]TAN X Y,LI Y,LIU J,et al.Face liveness detection from a single image with sparse low rank bilinear discriminative model[C]//Proceedings of European Conference on Computer Vision.Crete,Greece:Springer,2010:504-517.
[32]ZHANG Z W,WAN J J,LIU S F,et al.A face antispoofing database with diverse attacks[C]//Proc of IEEE the 5th IAPR International Conference on Biometrics.Piscataway,NJ:IEEE Press,2012:26-31.
[33]CHINGOVSKA I,ANJOS A,MARCEL S.On the effectiveness of local binary patterns inface anti-spoofing[C]//Proceedings of the 11thInternational Conference of the Biometrics Special Interest Group.Darmstadt,Germany:IEEE,2012:1-7.
[34]JIANG F L,LIU P C,ZHOU X D.A Review on face anti-spoofing[J].Acta Automatica Sinica,2019,11(5):1-24.
[35]LIU Y,JOURABLOO A,LIU X.Learning deep models for face anti-spoofing:Binary or auxiliary supervision[C]//Proceedings of the IEEE Conference Oncomputer Vision and Pattern Recognition.2018:389-398.
[36]BHARADWAJ S,DHAMECHA T I,VATSA M,et al.Computationally efficient face spoofingdetection with motion magnification[C]//Proceedings of Conference on Computer Vision and Pattern Recognition Workshops.USA:IEEE,2013:5-110.
[37]GHCHANDANA P.Image quality assessment for fake biometric detection[J].International Journal scientific Research & Development,2014,2(3):1417-1419.
[38]TIRUNAGARI S,POH N,WINDRIDGE D,et al.Detection of face spoofing using visual dynamics[J].Transactions on Information Forensics and Security,2015,10(4):762-777.
[39]KOMULAINEN J,HADID A,MATTI P.Face spoofing detection using dynamic texture[C]//Asian Conference on Computer Vision.Berlin:Springer,2012:5-6.
[40]ZHAO X C,LIN Y P,HEIKKILÄ J.Dynamic texture recognition using volume local binary count patterns with an application to 2D face spoofing detection[J].IEEE Transactions on Multimedia,2018,20(3):552-566.
[41]BENLAMOUDI A,AIADIK E,OUAFI A,et al.Face anti-spoofing based on frame difference and multilevel representation[J].Journal of Electronic Imaging,2017,26(4).
[42]BOULKENAFET Z,KOMULAINEN J,HADID A.Face anti-spoofing using speeded-up robust features and fisher vector encoding[J].IEEE Signal Processing Letters,2017,24(2):141-145.
[43]TU X K,FANG Y C.Ultra-deep neural network for face anti-spoofing[C]//Proceedings of International Conference on Neural In-formation Processing.Guangzhou,China:Springer,2017:686-695.
[44]LAKSHMINARAYANA N N,NARAYAN N,NAPP N,et al.A discriminative spatiotemporal mapping of face for liveness detectionn[C]//Proceedings of IEEE International Conference on Identity,Security and Behavior Analysis.New Delhi,India:IEEE,2017:1-7.
[45]REHMAN Y A U,PO L M,LIU M Y.LiveNet:Improving features generalization for face liveness detection using convolution neural networks[C]//Expert Systems with Applications.2018,108:159-169.
[46]NING X,LI W,WEI M,et al.Face anti-spoofing based on deep stackgeneralization networks[C]//7th International Conference on Pattern Recognition Applications and Methods.2018:317-323.
[47]LI L,FENG X Y,JIANG X Y,et al.Face anti-spoofing via deep local binary patterns[C]//Proceedings of IEEE International Conference on Image Processing.Beijing,China:IEEE,2017:101-105.
[48]LI H,HE P,WANG S,et al.Learning generalized deep feature representation for face anti-spoofing[J].IEEE Transactions on Information Forensics & Security,2018,13(99):2639-2652.
[49]MENOTTI D,CHIACHIA G,PINTO A,et al.Deep representation-ns for iris,face,and fingerprint spoofing detection[J].IEEE Transactions on Information Forensics and Security,2015,10(4):864-879.
[50]MATTA J.Face spoofing detection from single images usingtexture and local shape analysis[J].Iet Biometrics,2012,1(1):3-10.
[51]KOSE N,DUGELAY J L.Classification of captured and recaptured images to detect photograph spoofing[C]//Proceedings of International Conference on Informatics,Electronics and Vision.Dhaka,Bangladesh:IEEE,2012:1027-1032.
[52]YANG J,LEI Z,LIAO S,et al.Face liveness detection withcomponent dependent descriptor[C]//International Conference on Biometrics.IEEE,2013:1-6.
[53]ARASHLOO S R,KITTLER J,CHRISTMAS W.Face spoofing detection based on multiple descriptor fusion using multiscale dynamic binarized statistical image features[J].IEEE Transactions on Information Forensics & Security,2017,10(11):2396-2407.
[54]DE FREITAS PEREIRA T,ANJOS A,DEMARTINO J M,et al.Can face anti-spoofing countermeasures work in a real world scenario? [C]//Proceedings of International Conference on Biometrics.Madrid,Spain:IEEE,2013:1-8.
[55]PATEL K,HAN H,JAIN A K.Cross-databasef-ace anti-spoofing with robust feature representation[C]//Proceedings of Chinese Conference on Biometric Recognition.Chengdu,China:Springer,2016:611-619.
[56]LI H L,LI W,CAO H,et al.Un-supervised domain adaptation for face anti-spoofing[J].IEEE Transactions on Information Forensics and Security,2018,13(7):1794-1809.

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