期刊
MULTIMEDIA TOOLS AND APPLICATIONS
卷 78, 期 17, 页码 23847-23865出版社
SPRINGER
DOI: 10.1007/s11042-018-6242-8
关键词
Dimensionality reduction; Face recognition; Locality preserving projections; Discriminant maximum margin projections
类别
资金
- China Postdoctoral Science Foundation [2017 M611656]
- National Natural Science Fund of China [61502206, 61503195, 61462064, 61203243, 61603192, 61402231]
- Natural Science Fund of Jiangsu Province [BK20150523, BK20161580]
- University Natural Science Fund of Jiangsu Province of China [16KJB520020]
- Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology) [30916014107]
- 2011 Collaborative Innovation Center of Internet of Things Technology and Intelligent Systems(Minjiang University)
In this paper, we propose a novel dimensionality reduction algorithm called discriminant maximum margin projections (DMMP) for face recognition. By discovering both geometrical and discriminant structures of the data points, DMMP aims at finding a subspace that optimally preserves the local neighborhood information of the data set, as well as maximizes the margin between data points from different classes at each local area. Moreover, DMMP utilizes a equilibrium parameter to adjust the significance of the locality preserving property and margin distances of the data points. Finally, with the experiments used face recognition data sets, such as the ORL, Yale, and FERET face databases, the results prove that DMMP can attain a better effectiveness than most other advanced approaches.
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