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2014 Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition
Zhe-Zhou Yu, Yu-Hao Liu, Bin Li, Shu-Chao Pang, Cheng-Cheng Jia
J. Appl. Math. 2014: 1-10 (2014). DOI: 10.1155/2014/928051


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.


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Zhe-Zhou Yu. Yu-Hao Liu. Bin Li. Shu-Chao Pang. Cheng-Cheng Jia. "Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition." J. Appl. Math. 2014 1 - 10, 2014.


Published: 2014
First available in Project Euclid: 2 March 2015

zbMATH: 07131980
Digital Object Identifier: 10.1155/2014/928051

Rights: Copyright © 2014 Hindawi


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