Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 351-359.doi: 10.11896/jsjkx.210100173

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Local Weighted Representation Based Linear Regression Classifier and Face Recognition

YANG Zhang-jing1,2, WANG Wen-bo1, HUANG Pu1, ZHANG Fan-long1, WANG Xin1   

  1. 1 School of Information Engineering,Nanjing Audit University,Nanjing 211815,China
    2 Jiangsu Key Laboratory of Auditing Information Engineering,Nanjing Audit University,Nanjing 211815,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:YANG Zhang-jing ,born in 1979,associate professor.His main research inte-rests include computer vision and pattern recognition,etc.
    HUANG Pu,born in 1985,associate professor.His main research interests include machine learning and pattern recognition,etc.
  • Supported by:
    National Natural Science Foundation of China(U1831127),Industry University Research Cooperation Project in Jiangsu Province(BY2020033) and Qinglan Projects of Colleges and Universities of Jiangsu Province.

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Abstract: Linear regression classifier (LRC) is an effective image classification algorithm.However,LRC does not pay attention to the local structure information of data and ignores the differences among samples within the class,and the performance may degrade when the facial images contain variations in expression,illumination,angle and occlusion.To address this problem,a linear regression classifier based on local weighted representation (LWR-LRC) is proposed.Firstly,LWR-LRC constructs a weighted representative sample for each class of samples based on the similarity between test samples and all samples,then decomposes the test samples into linear combinations of weighted representative samples,finally classifies the test samples into the category with the largest reconstruction coefficient.LWR-LRC considers the local structure of samples,constructs the optimal representative samples of each class of samples,and uses the representative samples to calculate,which improves the robustness and greatly time cost.The experiments on AR,CMU PIE,FERET and GT datasets show that LWR-LRC is superior to NNC,SRC,LRC,CRC,MRC and LMRC.

Key words: Data representation, Face recognition, Linear regression, Manifold learning

CLC Number: 

  • TP391.4
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