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Content Provider | IET Digital Library |
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Author | Wu, Lian Xu, Wenbo Zhao, Jianchuan Cui, Zhongwei Zhao, Yong |
Abstract | Most methods for sparse representation are designed to be used in the original space. However, their performance is not always satisfactory especially when training samples are limited. According to the previous studies, more information can be obtained from samples in the feature space than those in the original space. The authors propose a novel kernel difference maximisation-based sparse representation method, and its remarkable performance in face recognition is demonstrated by the experiments. The proposed method converts all the samples into the feature space, and a test sample can be denoted as a representation with all the training samples’ linear combinations. Besides, a novel solution scheme for sparse representation is utilised to obtain the l 2 regularisation-based sparse solution. Finally, the classification of the test sample can be easily judged according to the representation result. The representation results of test samples from different classes obtained by their method are very different, making the classification of test samples easier. Besides, the proposed method is simpler than the related methods and does not require dictionary learning. |
Starting Page | 1074 |
Ending Page | 1079 |
Page Count | 6 |
Volume Number | 2020 |
e-ISSN | 20513305 |
Issue Number | Issue 11, Nov (2020) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2020/11 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1003 |
Journal | The Journal of Engineering |
Publisher | The Institution of Engineering and Technology |
Publisher Date | 2020-08-05 |
Access Restriction | Open |
Rights License | Creative Commons Attribution -Non Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) |
Subject Keyword | Computer Vision And Image Processing Technique Face Recognition Feature Extraction Feature Space Image Classification Image Recognition Image Representation Knowledge Engineering Technique L2 Regularisation-based Sparse Solution Learning in AI Novel Kernel Difference Maximisation-based Sparse Representation Method Optimisation Optimisation Technique Test Sample Classification Training Sample Linear Combinations |
Content Type | Text |
Resource Type | Article |
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