An improve face representation and recognition method based on graph regularized non-negative matrix factorization

  • Minghua WanEmail author
  • Zhihui Lai
  • Zhong Ming
  • Guowei Yang


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.


Non-negative matrix factorization (NMF) Graph embedded (GE) Face recognition Discriminant criterion (DC) 



This work is partially supported by National Key R&D Program Grant No. 2017YFC0804002, the National Science Foundation of China under Grant Nos. 61876213, 61462064, 6177227, 61861033, 61603192, the China Postdoctoral Science Foundation under Grant No. 2016 M600674, the Natural Science Fund of Jiangsu Province under Grants BK20161580, BK20171494 and China’s Jiangxi Province Natural Science Foundation (No. 20181BAB202022), and the Fund of China’s Jiangxi Provincial Department of Education (No. GJJ170599).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Minghua Wan
    • 1
    • 2
    Email author
  • Zhihui Lai
    • 2
  • Zhong Ming
    • 2
  • Guowei Yang
    • 1
  1. 1.School of Information EngineeringNanjing Audit UniversityNanjingPeople’s Republic of China
  2. 2.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenPeople’s Republic of China

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