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Recently non-negative matrix factorization (NMF) has received a lot of attentions in information retrieval, computer vision and pattern recognition. NMF aims to find two non-negative matrices whose product can well approximate the original matrix. The sizes of these two matrices are usually smaller than the original matrix. This results in a compressed version of the original data matrix. The solution of NMF yields a natural parts-based representation for the data. When NMF is applied for data representation, a major disadvantage is that it fails to consider the geometric structure in the data. Non-negative Matrix Factorization is a promising approach for face recognition in that it is capable of extracting the local features by factorizing the nonnegative matrix into two nonnegative matrices.

Index Terms: Matlab, source, code, face, facial, recognition, NMF, non-negative, matrix, factorization.

 

 

 

 

 

Figure 1. Non-negative components



A simple and effective source code for NMF Face Recognition System.



Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox is required.

Release
Date
Major features
1.0

2014.06.08



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The authors have no relationship or partnership with The Mathworks. All the code provided is written in Matlab language (M-files and/or M-functions), with no dll or other protected parts of code (P-files or executables). The code was developed with Matlab 2006a. Matlab Image Processing Toolbox is required. The code provided has to be considered "as is" and it is without any kind of warranty. The authors deny any kind of warranty concerning the code as well as any kind of responsibility for problems and damages which may be caused by the use of the code itself including all parts of the source code.

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