Computer Science ›› 2018, Vol. 45 ›› Issue (4): 273-277.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

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