Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 390-396.doi: 10.11896/jsjkx.210600217

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Face Anti-spoofing Algorithm Based on Texture Feature Enhancement and Light Neural Network

SHEN Chao1,2, HE Xi-ping1,2,3   

  1. 1 School of Management Science and Engineering,Chongqing Technology and Business University,Chongqing 400067,China
    2 National Research Base of Intelligent Manufacturing Service,Chongqing Technology and Business University,Chongqing 400067,China
    3 School of Artificial Intelligence,Chongqing Technology and Business University,Chongqing Engineering Laboratory for Detection,Control and Integrated System,Chongqing 400067,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:SHEN Chao,born in 1996,postgra-duate.His main research interests include face anti-spoofing and deep lear-ning.
    HE Xi-ping,born in 1968,Ph.D,professor.His main research interests include machine learning,data analysis and processing,computer vision,and information system security.
  • Supported by:
    Science and Technology Research Foundation of Chongqing Education Commission(KJQN201900833) and Scientific Research and Innovation Foundation of Chongqing,China(CYS21398).

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Abstract: Face anti-spoofing is an important part of face recognition,which is of great significance to the safety of related industries in reality,such as authentication,security key,financial payment and so on.The mainstream face anti-counterfeiting algorithm based on deep learning has achieved quite advanced results,but there are still some problems,such as too many model parameters increases the difficulty of actual deployment,and the generalization performance of light neural network structure is not good,etc.Aiming at the problems of poor generalization ability and too large parameters of the related face anti-spoofing algorithm.This paper proposes a texture feature enhancement method and a face anti-spoofing detection algorithm based on improved FeatherNet network.By enhancing the texture difference features of real and fake face information as the input of the backbone network.In the design of the backbone network,DropBlock module and multi-channel attention feature map branch are introduced.The generalization performance is enhanced while maintaining the speed.The designed algorithm shows good performance improvement in both data-set test and cross data-set test.

Key words: Face anti-spoofing, Light neural network, Multi-channel attention map, Texture enhancement

CLC Number: 

  • TP399
[1] WANG Z,ZHAO C,QIN Y,et al.Exploiting temporaland depth information for multi-frame face anti-spoofing[J].arXiv:abs/1811.05118,2018.
[2] ZHANG P,ZOU F H,WU Z W,et al.FeatherNets:Convolutional Neural Networks as Light as Feather[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW).Long Beach,CA,USA,2019:1574-1583.
[3] ZHANG X,ZHOU X,LIN M,et al.ShuffleNet:An Extremely Efficient Convolutional Neural Network for Mobile Devices[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City,UT,USA,2018:6848-6856.
[4] HOWARD A G,ZHU M,CHEN B,et al.MobileNets:Efficient Convolutional Neural Networks for Mobile Vision Applications[J].arXiv:abs/1704.04861,2017.
[5] PARKIN A,GRINCHUK O.Recognizing Multi-Modal FaceSpoofing With Face Recognition Networks[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW).Long Beach,CA,USA,2019:1617-1623.
[6] SHEN T,HUANG Y,TONG Z.FaceBagNet:Bag-Of-Local-Features Model for Multi-Modal Face Anti-Spoofing[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW).Long Beach,CA,USA,2019:1611-1616.
[7] PARKIN A,GRINCHUK O.Creating Artificial Modalities to Solve RGB Liveness[J].arXiv:abs/2006.16028,2020.
[8] SAJJADI M S M,SCHÖLKOPF B,HIRSCH M.EnhanceNet:Single Image Super-Resolution Through Automated Texture Synthesis[C]//2017 IEEE International Conference on Compu-ter Vision(ICCV).Venice,Italy,2017:4501-4510.
[9] IANDOLA F,MOSKEWICZ M,KARAYEV S,et al.DenseNet:Implementing Efficient ConvNet Descriptor Pyramids[J].arXiv:abs/1404.1869.2014.
[10] GHIASI G,LIN T Y,LE Q V.DropBlock:A regularizationmethod for convolutional networks[J].arXiv:abs/1810.12890,2018.
[11] SANDLER M,HOWARD A,ZHU M,et al.MobileNetV2:Inverted Residuals and Linear Bottlenecks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City,UT,USA,2018:4510-4520.
[12] HU J,SHEN L,ALBANIE S,et al.Squeeze-and-Excitation Networks[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence.2020:2011-2023.
[13] BHARADWAJ S,DHAMECHA T I,VATSA M,et al.Computationally efficient face spoofing detection with motion magnification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.2013:105-110.
[14] DE FREITAS PEREIRA T,ANJOS A,DE MARTINO J M,et al.Can face anti-spoofing counter measures work in a real world scenario? [C]//2013 International Conference on Biome-trics(ICB).2013:1-8.
[15] JOURABLOO A,LIU Y J,LIU X M.Face despoofing:Anti-spoofing via noise modeling[C]//Proceedings of the European Conference on Computer Vision(ECCV).2018:290-306.
[16] BOULKENAFET Z,KOMULAINEN J,HADID A.Face anti-spoofing based on color texture analysis[C]//IEEE IntelnationalConference on Image Processing(ICIP).2015:2636-2640.
[17] PINTO A,PEDRINI H,SCHWARTZ W R,et al.Face spoofing detection through visual codebooks of spectral temporal cubes[J].IEEE Transactions on Image Processing,2015,24(12):4726-4740.
[18] BOULKENAFET Z,KOMULAINEN J,HADID A.Facespoo-fing detection using colour texture analysis[J].IEEE Transactions on Information Forensics and Security,2016,11(8):1818-1830.
[19] LIU Y J,JOURABLOO A,LIU X M.Learning deep models for face anti-spoofing:Binary or auxiliary supervision[C]//Procee-dings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:389-398.
[20] YANG X,LUO W H,BAO L C,et al.Face anti-spoofing:Model matters,so does data[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019.
[21] WANG Z Z,ZHAO C X,QIN Y X,et al.Exploiting temporal and depth information for multi-frame face anti-spoofing[J].arXiv:abs/1811.05118,2018.
[22] SHARMA V,DIBA A,NEVEN D,et al.Classification DrivenDynamic Image Enhancement[J].arXiv:1710.07558,2018.
[23] HUANG J,ZHU P F,GENG M R,et al.Range Scaling Global U-Net for Perceptual Image Enhancement on Mobile Devices[C]//Computer Vision-ECCV 2018 Workshops.2018:230-242.
[24] KIM T,KIM Y,KIM I,et al.Basn:Enriching feature representation usingbipartite auxiliary supervisions for face anti-spoofing[C]//ICCV Workshops.2019.
[1] ZHUO Ya-qian, OU Bo. Face Anti-spoofing Algorithm for Noisy Environment [J]. Computer Science, 2021, 48(6A): 443-447.
[2] WU Xiao-li, HU Wei. Attention-based Hot Block and Saliency Pixel Convolutional Neural Network Method for Face Anti-spoofing [J]. Computer Science, 2021, 48(4): 316-324.
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