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L1-norm plus L2-norm sparse parameter for image recognition

OPTIK 2015年第23期126卷 4078-4082页

作者： Feng, Qingxiang Zhu, Qi Tang, Lin-Lin Pan, Jeng-Shyang Harbin Inst Technol Shenzhen Grad Sch Shenzhen 518055 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Jiangsu Peoples R China

In this paper, a new classifier based on both sparse representation classification (SRC) and simple-fast representation-based classifier (SFR), called L1-norm plus L2-norm sparse parameter (L1L2-SP) classifier, is proposed for image recognition. The SRC classifier only utilizes the distance between test image vector and the L1-norm sparse representation for classification. The SFR classifier uses the test image vector and the nearest image vector of class subspace to classify the test sample. However, the L1L2-SP classifier firstly computes the L1-norm sparse parameter and L1-norm sparse representation, which are used to compose a novel robust global space. Then the L1L2-SP utilizes the novel global space to get the L2-norm sparse parameter. In the last, L1L2-SP uses the sum of L1-norm sparse parameter and L2-norm sparse parameter for classification. A mass number of experiments on coil100 object database, eth80 object database, Oxford flower 17 database and FERET face database are used to assess the proposed classifier. The experimental results prove that the proposed approach achieves better recognition rate than the SRC classifier, SFR classifier, and several other classifiers. (C) 2015 Elsevier GmbH. All rights reserved.

关键词： Sparse representation classification Nearest neighbor classifier simple-fast representation-based classifier Image recognition

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Double linear regression classification for face recognition

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JOURNAL OF MODERN OPTICS 2015年第4期62卷 288-295页

作者： Feng, Qingxiang Zhu, Qi Tang, Lin-Lin Pan, Jeng-Shyang Harbin Inst Technol Shenzhen Grad Sch Shenzhen Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Jiangsu Peoples R China

A new classifier designed based on linear regression classification (LRC) classifier and simple-fast representation-based classifier (SFR), named double linear regression classification (DLRC) classifier, is proposed for image recognition in this paper. As we all know, the traditional LRC classifier only uses the distance between test image vectors and predicted image vectors of the class subspace for classification. And the SFR classifier uses the test image vectors and the nearest image vectors of the class subspace to classify the test sample. However, the DLRC classifier computes out the predicted image vectors of each class subspace and uses all the predicted vectors to construct a novel robust global space. Then, the DLRC utilizes the novel global space to get the novel predicted vectors of each class for classification. A mass number of experiments on AR face database, JAFFE face database, Yale face database, Extended YaleB face database, and PIE face database are used to evaluate the performance of the proposed classifier. The experimental results show that the proposed classifier achieves better recognition rate than the LRC classifier, SFR classifier, and several other classifiers.

关键词： face image representation and recognition linear regression classification nearest neighbor classifier simple-fast representation-based classifier

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