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Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition

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  • Zhe-Zhou Yu
  • Yu-Hao Liu
  • Bin Li
  • Shu-Chao Pang
  • Cheng-Cheng Jia

Abstract

In a real world application, we seldom get all images at one time. Considering this case, if a company hired an employee, all his images information needs to be recorded into the system; if we rerun the face recognition algorithm, it will be time consuming. To address this problem, In this paper, firstly, we proposed a novel subspace incremental method called incremental graph regularized nonnegative matrix factorization (IGNMF) algorithm which imposes manifold into incremental nonnegative matrix factorization algorithm (INMF); thus, our new algorithm is able to preserve the geometric structure in the data under incremental study framework; secondly, considering we always get many face images belonging to one person or many different people as a batch, we improved our IGNMF algorithms to Batch‐IGNMF algorithms (B‐IGNMF), which implements incremental study in batches. Experiments show that (1) the recognition rate of our IGNMF and B‐IGNMF algorithms is close to GNMF algorithm while it runs faster than GNMF. (2) The running times of our IGNMF and B‐IGNMF algorithms are close to INMF while the recognition rate outperforms INMF. (3) Comparing with other popular NMF‐based face recognition incremental algorithms, our IGNMF and B‐IGNMF also outperform then both the recognition rate and the running time.

Suggested Citation

  • Zhe-Zhou Yu & Yu-Hao Liu & Bin Li & Shu-Chao Pang & Cheng-Cheng Jia, 2014. "Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnljam:v:2014:y:2014:i:1:n:928051
    DOI: 10.1155/2014/928051
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    References listed on IDEAS

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    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
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