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An improve face representation and recognition method based on graph regularized non-negative matrix factorization
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DOI：10.1007/s11042-019-7454-2.png)
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摘要
En 中文
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.
Keyword:
Non-negative matrix factorization (NMF)
Graph embedded (GE)
Face recognition
Discriminant criterion (DC)
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期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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