A weighted sparse neighbor representation based on Gaussian kernel function to face recognition

Abstract Currently, sparse representation was widely used in face recognition. However, traditional sparse representation method cannot effectively consider the effect of different weight of training samples When reconstruct the test samples. In this paper, a weighted sparse neighbor representation based on Gaussian kernel function model is presented to resolve above problems. Firstly, K nearest training samples is selected for constructing a new training dictionary according to the Euclidean distances between the test samples and training samples. Then, a weight is given to each sparse coefficient of new training sample. Above sparse coefficient is solved by norm L1 minimization method. Finally, recognition task is performed by the minimum reconstruction error of sparse coefficient. Experimental results illustrate that, the proposed algorithm achieves 96.64% correct recognition rate, which is significantly higher than the various existing comparison methods.

[1]  Jian Yang,et al.  LPP solution schemes for use with face recognition , 2010, Pattern Recognit..

[2]  Ran He,et al.  Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Wei Chen,et al.  A novel sparse representation method based on virtual samples for face recognition , 2012, Neural Computing and Applications.

[4]  Xiaoyang Tan,et al.  Pattern Recognition , 2016, Communications in Computer and Information Science.

[5]  Honggang Zhang,et al.  Local Sparse Representation Based Classification , 2010, 2010 20th International Conference on Pattern Recognition.

[6]  Xuelong Li,et al.  Face Sketch–Photo Synthesis and Retrieval Using Sparse Representation , 2012, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  Thomas S. Huang,et al.  Joint dynamic sparse representation for multi-view face recognition , 2012, Pattern Recognit..

[8]  Lei Zhang,et al.  Sparse neighbor representation for classification , 2012, Pattern Recognit. Lett..

[9]  Huorong Ren,et al.  Nonparametric subspace analysis fused to 2DPCA for face recognition , 2014 .

[10]  Jian Yang,et al.  Complete large margin linear discriminant analysis using mathematical programming approach , 2013, Pattern Recognit..

[11]  Yong Liu,et al.  Multiclass AdaBoost ELM and Its Application in LBP Based Face Recognition , 2015 .

[12]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[13]  Qing Guo,et al.  A Lower Extremity Exoskeleton: Human-Machine Coupled Modeling, Robust Control Design, Simulation, and Overload-Carrying Experiment , 2015 .

[14]  Jian Yang,et al.  Enhanced iterative projection for subclass discriminant analysis under EM-alike framework , 2014, Pattern Recognit..

[15]  Yong Du,et al.  Generating virtual training samples for sparse representation of face images and face recognition , 2016 .

[16]  Belur V. Dasarathy,et al.  Nearest neighbor (NN) norms: NN pattern classification techniques , 1991 .

[17]  Jian Yang,et al.  K Nearest Neighbor Based Local Sparse Representation Classifier , 2010, 2010 Chinese Conference on Pattern Recognition (CCPR).

[18]  Zhen Li,et al.  Enhanced Asymmetric Bilinear Model for Face Recognition , 2015, Int. J. Distributed Sens. Networks.

[19]  Jianhang Liu,et al.  A Low Computational Complexity SML Estimation Algorithm of DOA for Wireless Sensor Networks , 2015, Int. J. Distributed Sens. Networks.

[20]  Xiao-Yuan Jing,et al.  Orthogonal sparsity preserving projections for feature extraction , 2010, 2010 International Conference on Wavelet Analysis and Pattern Recognition.

[21]  Zhong Jin,et al.  Global Sparse Representation Projections for Feature Extraction and Classification , 2009, 2009 Chinese Conference on Pattern Recognition.

[22]  Xue Li,et al.  Face recognition using class specific dictionary learning for sparse representation and collaborative representation , 2016, Neurocomputing.

[23]  Ching Y. Suen,et al.  Robust face recognition based on dynamic rank representation , 2016, Pattern Recognit..

[24]  Xuelong Li,et al.  Bayesian Tensor Approach for 3-D Face Modeling , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[25]  Jiexin Pu,et al.  Face Recognition Via Weighted Two Phase Test Sample Sparse Representation , 2013, Neural Processing Letters.

[26]  M. Belić,et al.  Raman solitons in nanoscale optical waveguides, with metamaterials, having polynomial law non-linearity , 2016 .

[27]  Mohamed A. El-Sayed,et al.  Study of Similarity Measures with Linear Discriminant Analysis for Face Recognition , 2015 .

[28]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[30]  Geng Yang,et al.  Adaptive linear discriminant regression classification for face recognition , 2016, Digit. Signal Process..

[31]  Dacheng Tao,et al.  Sparse transfer learning for interactive video search reranking , 2012, TOMCCAP.