Neighborhood and center difference-based-LBP for face recognition

This research paper introduces the novel local binary pattern (LBP) variant for face recognition (FR) called as neighborhood and center difference-based-LBP (NCDB-LBP). In NCDB-LBP, the 4 labeled function is proposed to capture the robust features from 3 × 3 pixel window. For each neighborhood position , 2 first-order derivatives are computed, first computed between the adjacent neighborhood and the current neighborhood and the second computed between the center pixel and the current neighborhood. Employing the proposed function between the 2 first-order derivatives (produced from each neighborhood position) eventually results in 4 labeled window. All 8 neighborhoods are then placed in the 1 × 8 pixel window from which the 4 different binary patterns are produced. This concept is performed in both anticlockwise (ac) and clockwise (c) directions, termed as NCDB-LBP ac and NCDB-LBP c descriptors. After binary patterns are encoded for each pixel position, the 4 transformed images are produced from ac direction and 4 from the c direction. All the respective directional transformed images are then divided into 3 × 3 subregions for histogram extraction. The combined histograms from all the respective subregions are the entire feature size of the NCDB-LBP ac and NCDB-LBP c descriptors. To reduce the feature size, PCA and FLDA are utilized. Finally, classification is performed by SVMs and NN. The proposed FR approach is tested on ORL, GT, JAFFE, Yale, YB and EYB databases. The proposed FR approach achieves encouraging results.

[1]  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..

[2]  Jian Yang,et al.  Discriminative Block-Diagonal Representation Learning for Image Recognition , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Xuyang Wang,et al.  State Generation Method for Humanoid Motion Planning Based on Genetic Algorithm , 2012 .

[4]  J KriegmanDavid,et al.  Eigenfaces vs. Fisherfaces , 1997 .

[5]  Yang Zhao,et al.  Completed robust local binary pattern for texture classification , 2013, Neurocomputing.

[6]  Jeng-Shyang Pan,et al.  Superimposed Sparse Parameter Classifiers for Face Recognition , 2017, IEEE Transactions on Cybernetics.

[7]  Bing Liu,et al.  Manifold regularized extreme learning machine , 2015, Neural Computing and Applications.

[8]  Marko Heikkilä,et al.  Description of interest regions with local binary patterns , 2009, Pattern Recognit..

[9]  Stan Z. Li,et al.  Manifold Learning and Applications in Recognition , 2005 .

[10]  Jiri Matas,et al.  Face verification via error correcting output codes , 2003, Image Vis. Comput..

[11]  Bailing Zhang,et al.  Robust Face Recognition by Hierarchical Kernel Associative Memory Models Based on Spatial Domain Gabor Transforms , 2006, J. Multim..

[12]  Wanquan Liu,et al.  Face recognition based on curvelets and local binary pattern features via using local property preservation , 2014, J. Syst. Softw..

[13]  Kin-Man Lam,et al.  Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image , 2006, IEEE Transactions on Image Processing.

[14]  Roberto Cipolla,et al.  A methodology for rapid illumination-invariant face recognition using image processing filters , 2009, Comput. Vis. Image Underst..

[15]  Jiawei Han,et al.  Orthogonal Laplacianfaces for Face Recognition , 2006, IEEE Transactions on Image Processing.

[16]  S. Jahan.  On Dimension Reduction Using Supervised Distance Preserving Projection for Face Recognition , 2018 .

[17]  Mohammad T. Manzuri Shalmani,et al.  Three-dimensional modular discriminant analysis (3DMDA): A new feature extraction approach for face recognition , 2011, Comput. Electr. Eng..

[18]  Quan-Sen Sun,et al.  Laplacian multiset canonical correlations for multiview feature extraction and image recognition , 2015, Multimedia Tools and Applications.

[19]  Adel Hafiane,et al.  Joint Adaptive Median Binary Patterns for texture classification , 2015, Pattern Recognit..

[20]  Xuelong Li,et al.  Regularized Label Relaxation Linear Regression , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[21]  Qi Zhang,et al.  FFT Consolidated Sparse and Collaborative Representation for Image Classification , 2018 .

[22]  Chokri Ben Amar,et al.  Statistical binary patterns and post-competitive representation for pattern recognition , 2018, Int. J. Mach. Learn. Cybern..

[23]  L. Sumalatha,et al.  Face Recognition based on Cross Diagonal Complete Motif Matrix , 2018 .

[24]  Paul W. Fieguth,et al.  Extended local binary patterns for texture classification , 2012, Image Vis. Comput..

[25]  Ioannis Pitas,et al.  Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines , 2007, IEEE Transactions on Image Processing.

[26]  Yun Fu,et al.  Image Classification Using Correlation Tensor Analysis , 2008, IEEE Transactions on Image Processing.

[27]  Jinhui Tang,et al.  Robust Structured Nonnegative Matrix Factorization for Image Representation , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[28]  Jinhui Tang,et al.  Unsupervised Feature Selection via Nonnegative Spectral Analysis and Redundancy Control , 2015, IEEE Transactions on Image Processing.

[29]  Matti Pietikäinen,et al.  Extended local binary patterns for face recognition , 2016, Inf. Sci..

[30]  Xiaolan Fu,et al.  Face Recognition and Micro-expression Recognition Based on Discriminant Tensor Subspace Analysis Plus Extreme Learning Machine , 2014, Neural Processing Letters.

[31]  David J. Kriegman,et al.  Acquiring linear subspaces for face recognition under variable lighting , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Balasubramanian Raman,et al.  Multi-quantized local binary patterns for facial gender classification , 2016, Comput. Electr. Eng..

[33]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[34]  Kin-Man Lam,et al.  An efficient illumination normalization method for face recognition , 2006, Pattern Recognit. Lett..

[35]  Zhenhua Guo,et al.  Local directional derivative pattern for rotation invariant texture classification , 2011, Neural Computing and Applications.

[36]  Balasubramanian Raman,et al.  Local neighborhood difference pattern: A new feature descriptor for natural and texture image retrieval , 2018, Multimedia Tools and Applications.

[37]  Mourad Moussa,et al.  A Novel Face Recognition Approach Based on Genetic Algorithm Optimization , 2018 .

[38]  Shouyi Yin,et al.  A High Precision Feature Based on LBP and Gabor Theory for Face Recognition , 2013, Sensors.

[39]  Zhenhua Guo,et al.  Face recognition by sparse discriminant analysis via joint L2, 1-norm minimization , 2014, Pattern Recognit..

[40]  Matti Pietikäinen,et al.  Median Robust Extended Local Binary Pattern for Texture Classification , 2016, IEEE Trans. Image Process..