期刊
MULTIMEDIA TOOLS AND APPLICATIONS
卷 78, 期 15, 页码 22109-22126出版社
SPRINGER
DOI: 10.1007/s11042-019-7454-2
关键词
Non-negative matrix factorization (NMF); Graph embedded (GE); Face recognition; Discriminant criterion (DC)
类别
资金
- National Key RD Program [2017YFC0804002]
- National Science Foundation of China [61876213, 61462064, 6177227, 61861033, 61603192]
- China Postdoctoral Science Foundation [2016 M600674]
- Natural Science Fund of Jiangsu Province [BK20161580, BK20171494]
- China's Jiangxi Province Natural Science Foundation [20181BAB202022]
- Fund of China's Jiangxi Provincial Department of Education [GJJ170599]
Based on recently proposed Non-negative Matrix Factorization (NMF) and Graph Embedded (GE) techniques with Discriminant Criterion (DC), we present in this paper a new algorithm of Face Representation and Recognition (FRR) called Discriminant Graph Regularized Non-negative Matrix Factorization (DGNMF) for dimensionality reduction (DR). Here, we firstly encode the geometrical class information by constructing an affinity graph using the DGNMF algorithm. After this, we determine a matrix factorization which adequately represents the graph structure. Finally, we conduct experiments to prove that DGNMF provides a better representation and achieves higher face recognition rates than previous approaches.
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