Title | Kernel difference maximisation-based sparse representation for more accurate face recognition |
Authors | Wu, Lian Xu, Wenbo Zhao, Jianchuan Cui, Zhongwei Zhao, Yong |
Affiliation | Guizhou Educ Univ, Sch Math & Big Data, Guiyang 550018, Peoples R China Guizhou Educ Univ, Big Data Sci & Intelligent Engn Res Inst, Guiyang 550018, Peoples R China Peking Univ, Sch Elect & Comp Engn, Shenzhen Grad Sch, Shenzhen 518055, Peoples R China |
Issue Date | Nov-2020 |
Publisher | JOURNAL OF ENGINEERING-JOE |
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. |
URI | http://hdl.handle.net/20.500.11897/603221 |
DOI | 10.1049/joe.2019.1003 |
Indexed | CCR ESCI IC |
Appears in Collections: | 信息工程学院 |