Computer Science ›› 2016, Vol. 43 ›› Issue (6): 303-307.doi: 10.11896/j.issn.1002-137X.2016.06.060

Previous Articles     Next Articles

Forward and Unsupervised Convolutional Neural Network Based Face Representation Learning Method

ZHU Tao, REN Hai-jun and HONG Wei-jun   

  • Online:2018-12-01 Published:2018-12-01

Abstract: The existing face representation learning methods based on deep convolutional neural network demand massive labeled face dataset.In real-world application,it is difficult to precistly annotate the labels of face dataset.In this paper,an unsupervised forward convolutional neural network based face representation learning algorithm was proposed.By design,virtual labels of training samples were abtained based on K-means clustering and then convolution kernels were learnt by linear discriminant analysis.The network architecture in this paper is simple and effecitve and does not need back propagation during training,so its training speed is much quicker than supervised deep convolution neural network.The experimental results demonstrate that the proposed method in this paper out performs the state-of-the-art unsupervised feature learning algorithm and local feature descriptors in both real-world Labeled Face in the Wild (LFW) dataset and the classical controlled Feret dataset.

Key words: Unsupervised learning,Convolutional neural network,Face recognition,Representation learning

[1] Zhao W,Chellappa R,Phillips P J,et al.Face recognition:A li-terature survey[J].ACM Computer Surveys (CSUR),2003,35(4):399-458
[2] Turk M,Pentland A.Eigenfaces for recognition[J].Journal of cognitive neuroscience,1991,3(1):71-86
[3] Belhumeur P N,Hespanha J P.Eigenfaces vs.fisherfaces:Re-cognition using class specific linear projection[J].IEEE Tran-sactions on Pattern Analysis and Machine Intelligence,1997,19(7):711-720
[4] Liu C,Wechsler H.Gabor feature based classification using the enhanced fisher linear discriminant model for face[J].IEEE Transactions on Image Processing,2002,11(4):467-476
[5] Ahonen T,Hadid A,Pietikainen M.Face Description with Local Binary Patterns:Application to Face Recogniton[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2006,28(12):2037-2041
[6] Vu N S,Caplier A.Enhanced patterns of oriented edge magnitudes for face revognition and image matching[J].IEEE Tran-sactions on Image Processing,2011,21(3):1352-1365
[7] M Bicego,A Lagorio,E Grosso,et al.On the use of SIFT features for face authentication[C]∥Computer Vision and Pattern Recognition Workshop,2006(CVPRW’06).2006:35
[8] Chan T H,Jia K,Gao S,et al.PCANet:A Simple Deep Learning Baseline for Image Classification?[J].IEEE Transactions on Image Processing,2014,4(12):1
[9] Coates A,Ng A Y.Learning Feature Representation Using K-means[M].Springer Berlin Heidelberg,2012
[10] Hartigan J A,Wong M A.Algorithm AS 136:A K-means clustering algorithm[J].Applied Statistics,1979,28(1):100-108
[11] Cui Z,Li W,Xu D,et al.Fusing Robust Face Region Descriptors via Multiple Metric Learning for Face Recognition in the Wild[C]∥Proc.of the 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Portland,OR:IEEE,2013:3554-3561
[12] Huang G B,Mattar M,Berg T,et al.Labeled faces in the wild:A database for studying face recognition in unconstrained environments[C]∥Proc.of the Workshop on Faces in ‘Real-Life’ Images:Detection,Alignment,and Recognition.Marseille,France:Springer Berlin Heidelberg,2008:864-877
[13] Sun Y,Chen Y,Wang X,et al.Deep learning face representation by joint identification-verification[C]∥Proc.of the Advances in Neural Information Processing Systems.2014:1988-1996
[14] Krizhevsky A,Sutskever I,Hinton G E.Imagenet classification with deep convolutional neural networks[C]∥Proc.of the Advances in Neural Information Processing Systems.2012:1097-1105
[15] Taigman Y,Yang M,Ranzato M A,et al.Deepface:Closing the gap to human-level performance in face verification[C]∥Proc.of the 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Columbus,OH:IEEE,2014:1701-1708
[16] Sun Y,Chen Y,Wang X,et al.Deep learning face representation by joint identification-verification[C]∥Proc.of the Advances in Neural Information Processing Systems.2014:1988-1996
[17] Phillips P J,Moon H,Rauss P J,et al.The FERET evaluation methodology for face recognition algorithms[C]∥ IEEE Computer Society Conference on Computer Vision and Pattern Re-cognition.1997:137-143
[18] Tan X,Triggs B.Enhanced local texture feature sets for face recognition under difficult lighting conditions[J].IEEE TIP,2010,19(6):1635-1650
[19] Vu N S,Caplier A.Enhanced patterns of oriented edge magnitudes for face recognition and image matching[J].IEEE TIP,2012,21(3):1352-1368
[20] Xie P,Wu X J.Modular Multilinear Principal Component Analysis and Application in Face Recognition[J].Computer Science,2015,42(3):274-279(in Chinese) 谢佩,吴小俊.分块多线性主成分分析及其在人脸识别中的应用研究[J].计算机科学,2015,42(3):274-279
[21] Tian Hua,Pu Tian-yin.Improved ASM localization method for human facial features[J].Journal of Chongqing Unversity Posts and Telecommunications(Natural Science Edition),2014,6(1):124-130(in Chinese) 田华,蒲天银.一种改进的ASM人脸特征点定位方法[J].重庆邮电大学学报(自然科学版),2014,6(1):124-130

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!