Learning more distinctive representation by enhanced PCA network

Abstract Subspace learning approaches extract features by a simple linear transformation, which can viewed as a shallow network, and they cannot reveal the deep structure embedded in pixels of image. To solve this problem, a deep principal component analysis (PCA) network, namely enhanced PCA Network (EPCANet), is proposed to explore more distinctive representation for face images. EPCANet adds a spatial pooling layer between the first layer and second layer in the PCANet. The spatial pooling layer reveals more spatial and distinctive information by down-sampling or pixel offset for the first layer output and original images. Extensive experimental results in several databases illustrate the efficiency of our proposed methods.

[1]  Gang Wang,et al.  Face recognition using Deep PCA , 2013, 2013 9th International Conference on Information, Communications & Signal Processing.

[2]  Dong Yu,et al.  Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[3]  Jiwen Lu,et al.  PCANet: A Simple Deep Learning Baseline for Image Classification? , 2014, IEEE Transactions on Image Processing.

[4]  Yurong Liu,et al.  A survey of deep neural network architectures and their applications , 2017, Neurocomputing.

[5]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Li Deng,et al.  Three Classes of Deep Learning Architectures and Their Applications: A Tutorial Survey , 2012 .

[7]  Lei Tian,et al.  Stacked PCA Network (SPCANet): An effective deep learning for face recognition , 2015, 2015 IEEE International Conference on Digital Signal Processing (DSP).

[8]  Jean Ponce,et al.  A Theoretical Analysis of Feature Pooling in Visual Recognition , 2010, ICML.

[9]  Fabian J. Theis,et al.  Denoising using local projective subspace methods , 2006, Neurocomputing.

[10]  Geoffrey E. Hinton.  A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.

[11]  Rob Fergus,et al.  Stochastic Pooling for Regularization of Deep Convolutional Neural Networks , 2013, ICLR.

[12]  Trevor Darrell,et al.  Beyond spatial pyramids: Receptive field learning for pooled image features , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[14]  Fuad E. Alsaadi,et al.  A switching delayed PSO optimized extreme learning machine for short-term load forecasting , 2017, Neurocomputing.

[15]  Jingjing Liu,et al.  Two-dimensional margin, similarity and variation embedding , 2012, Neurocomputing.

[16]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[17]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[18]  Ayman M. Eldeib,et al.  Breast cancer classification using deep belief networks , 2016, Expert Syst. Appl..

[19]  Shuicheng Yan,et al.  Neighborhood preserving embedding , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[20]  Hao Wang,et al.  Adaptive control of a class of switched nonlinear discrete-time systems with unknown parameter , 2016, Neurocomputing.

[21]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  Brian C. Lovell,et al.  Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference , 2009, ICB.

[23]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[24]  Youngwook Kim,et al.  Human Detection and Activity Classification Based on Micro-Doppler Signatures Using Deep Convolutional Neural Networks , 2016, IEEE Geoscience and Remote Sensing Letters.

[25]  Zidong Wang,et al.  Inferring nonlinear lateral flow immunoassay state-space models via an unscented Kalman filter , 2016, Science China Information Sciences.

[26]  Hua Yu,et al.  A direct LDA algorithm for high-dimensional data - with application to face recognition , 2001, Pattern Recognit..

[27]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Nicolas Le Roux,et al.  Ask the locals: Multi-way local pooling for image recognition , 2011, 2011 International Conference on Computer Vision.

[29]  B Anton,et al.  Immunohistochemical localization of ORL‐1 in the central nervous system of the rat , 1996, The Journal of comparative neurology.

[30]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[31]  Xiaohui Xie,et al.  Handwritten Hangul recognition using deep convolutional neural networks , 2014, International Journal on Document Analysis and Recognition (IJDAR).

[32]  Tara N. Sainath,et al.  Deep Convolutional Neural Networks for Large-scale Speech Tasks , 2015, Neural Networks.

[33]  Christian Igel,et al.  Training RBMs based on the signs of the CD approximation of the log-likelihood derivatives , 2011, ESANN.

[34]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[35]  Lei Tian,et al.  Multiple scales combined principle component analysis deep learning network for face recognition , 2016, J. Electronic Imaging.

[36]  Jun Wang,et al.  LRSR: Low-Rank-Sparse representation for subspace clustering , 2016, Neurocomputing.

[37]  H. Bourlard,et al.  Auto-association by multilayer perceptrons and singular value decomposition , 1988, Biological Cybernetics.

[38]  Fuad E. Alsaadi,et al.  Deep Belief Networks for Quantitative Analysis of a Gold Immunochromatographic Strip , 2016, Cognitive Computation.

[39]  Feiping Nie,et al.  On the schatten norm for matrix based subspace learning and classification , 2016, Neurocomputing.

[40]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[41]  David Zhang,et al.  Directional independent component analysis with tensor representation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[42]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.