Computer Science ›› 2018, Vol. 45 ›› Issue (4): 273-277, 284.doi: 10.11896/j.issn.1002-137X.2018.04.046

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Facial Multi-landmarks Localization Based on Single Convolution Neural Network

ZHU Hong, LI Qian-mu and LI De-qiang   

  • Online:2018-04-15 Published:2018-05-11

Abstract: Facial landmarks localization methods using deep learning network technology have achieved prominent effect.However,the localization of larger number of facial landmarks still has lots of challenges due to the complex diversities in face images caused by pose,expression,illumination and occlusion,etc.The existing deep learning methods for face and mark localization are based on cascaded networks or tasks-constrained deep convolutional network(TCDCN),which are complicated and difficult to train.To solve these problems,a new method of facial multi-landmarks location based on single convolution neural network was proposed.Unlike cascaded networks,the network consists of three stacks,and each group consists of two convolutional layers and a max-pooling layer.This network structure can extract more global high-level features,which express the facial landmarks more precisely.Extensive experiments show that the approach outperforms the existing methods in the complex conditions such as pose,illumination,expression and occlusion.

Key words: Deep learning,Convolution neural network,Facial landmarks localization,Data augmentation,Unconstrained condition

[1] TAIGMAN Y,YANG M,RANZATO M,et al.DeepFace:Clo-sing the Gap to Human-Level Performance in Face Verification[C]∥IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2014.
[2] SUN Y,CHEN Y,WANG X,et al.Deep learning face representation by joint identification-verification[C]∥Neural Information Processing Systems.Canada:MIT Press,2014.
[3] SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions[C]∥IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2015.
[4] SUN Y,WANG X,TANG X.Deeply learned face representations are sparse,selective,and robust[C]∥IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2015.
[5] COOTES T F,TAYLOR C J,COOPER D H,et al.Active shape models-their training and application[J].Computer Vision & Image Understanding,1995,61(1):38-59.
[6] GU L,KANADE T.A Generative Shape Regularization Model for Robust Face Alignment[C]∥European Conference on Computer Vision(ECCV).France:Springer-Verlag,2008.
[7] COOTES T F,EDWARDS G J,TAYLOR C J.Active appea-rance models[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2001,23(6):681-685.
[8] MATTHEWS I,BAKER S.Active Appearance Models Revisited[J].International Journal of Computer Vision,2004,60(2):135-164.
[9] XIONG X,TORRE F D L.Supervised Descent Method and Its Applications to Face Alignment[C]∥IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2013.
[10] CAO X,WEI Y,WEN F,et al.Face Alignment by ExplicitShape Regression[J].International Journal of Computer Vision,2014,107(2):177-190.
[11] BURGOS-ARTIZZU X P,PERONA P,DOLLAR P.RobustFace Landmark Estimation under Occlusion[C]∥IEEE International Conference on Computer Vision.New York:IEEE Press,2013.
[12] SUN Y,WANG X,TANG X.Deep Convolutional Network Cascade for Facial Point Detection[C]∥IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2013.
[13] ZHANG Z,LUO P,CHEN C L,et al.Facial Landmark Detection by Deep Multi-task Learning[C]∥European Conference on Computer Vision(ECCV).Zurich:Springer-Verlag,2014.
[14] PARKHI O M,VEDALDI A,ZISSERMAN A.Deep Face Re-cognition[C]∥British Machine Vision Conference(BMVC).UK:Springer-Verlag,2015.
[15] SAGONAS C,TZIMIROPOULOS G,ZAFEIRIOU S,et al.A Semi-automatic Methodology for Facial Landmark Annotation[C]∥IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2013.
[16] KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet classification with deep convolutional neural networks[C]∥International Conference on Neural Information Processing Systems.Lake Tahoe:Curran Associates Inc,2012:1097-1105.
[17] WU X,HE R,SUN Z.A Lightened CNN for Deep Face Representation[C]∥IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2015.
[18] SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-Scale Image Recognition[J].Computer Scie-nce,2015,47(4):1409-1556.
[19] SZEGEDY C,VANHOUCKE V,IOFFE S,et al.Rethinking the Inception Architecture for Computer Vision[C]∥IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2016:2818-2826.
[20] HE K,ZHANG X,REN S,et al.Deep Residual Learning for Ima-ge Recognition[C]∥ IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2016:770-778.
[21] BELHUMEUR P N,JACOBS D W,KRIEGMAN D J,et al.Localizing parts of faces using a consensus of exemplars[C]∥IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2011.
[22] LE V,BRANDT J,LIN Z,et al.Interactive facial feature localization[C]∥European Conference on Computer Vision(ECCV).Italy:Springer-Verlag,2012.
[23] RAMANAN D,ZHU X.Face detection,pose estimation,andlandmark localization in the wild[C]∥IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2012.
[24] MESSER K,MATAS J,KITTLER J,et al.Xm2vtsdb:the extended m2vts database[C]∥Second International Conference on Audioand Video-based Biometric Person Authentication.Zurich:Springer-Verlag,1999.
[25] CHEN T.MXNet:A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems[J].Statistics,2015,2(12):87-129.
[26] REN S,CAO X,WEI Y,et al.Face Alignment at 3000 FPS via Regressing Local Binary Features[C]∥IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2014.
[27] ZHANG J,SHAN S,KAN M,et al.Coarse-to-Fine Auto-En-coder Networks(CFAN) for Real-Time Face Alignment[C]∥European Conference on Computer Vision(ECCV).Zurich:Springer-Verlag,2014.
[28] KAZEMI V,SULLIVAN J.One millisecond face alignment with an ensemble of regression trees[C]∥European Conference on Computer Vision(ECCV).Zurich:Springer-Verlag,2014.
[29] ZHU S Z,LI C,CHEN C L,et al.Face alignment by coarse-to-fine shape searching[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.New York:IEEE Press,2015.

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