Abstract
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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Acknowledgements
This research was supported by China Postdoctoral Science Foundation funded project (Grant Nos. 2017 M611656), the National Natural Science Fund of China (Grant Nos. 61502206,61503195, 61462064, 61203243, 61603192, 61402231), the Natural Science Fund of Jiangsu Province (Grant No. BK20150523 and BK20161580), the University Natural Science Fund of Jiangsu Province of China (Grant No. 16KJB520020), the Project supported by the Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology) (Grant No. 30916014107), and 2011 Collaborative Innovation Center of Internet of Things Technology and Intelligent Systems(Minjiang University).
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Yang, Z., Huang, P., Wan, M. et al. Discriminant maximum margin projections for face recognition. Multimed Tools Appl 78, 23847–23865 (2019). https://doi.org/10.1007/s11042-018-6242-8
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DOI: https://doi.org/10.1007/s11042-018-6242-8