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Dimensionality reduction greatly facilitates pattern classification. Various techniques, linear and nonlinear, have been widely proposed and used for dimensionality reduction in face recognition systems. Principle Component Analysis (PCA) has proved to be a simple and efficient linear method; while many nonlinear methods such as kernel PCA, have been proposed recently. Nonlinear principal component analysis (NLPCA) is commonly seen as a nonlinear generalization of standard principal component analysis (PCA). It generalizes the principal components from straight lines to curves (nonlinear). Thus, the subspace in the original data space which is described by all nonlinear components is also curved. Nonlinear PCA can be achieved by using a neural network with an autoassociative architecture also known as autoencoder, replicator network, bottleneck or sandglass type network. Such autoassociative neural network is a multi-layer perceptron that performs an identity mapping, meaning that the output of the network is required to be identical to the input. However, in the middle of the network is a layer that works as a bottleneck in which a reduction of the dimension of the data is enforced. This bottleneck-layer provides the desired component values (scores). We have developed a simple algorithm that uses this nonlinear dimensionality reduction for face recognition. This approach does not require the detection of any reference point and it can be used for real-time applications.

Code for NLPCA has been developed by Matthias Scholz and it is available at http://www.nlpca.de.

Index Terms: Matlab, source, code, face, recognition, matching, PCA, NLPCA, nonlinear, dimensionality, reduction.

 

 

 

 

Figure 1. Dimensionality reduction



A simple and effective source code for Face Recognition Based on Nonlinear PCA.

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

Release
Date
Major features
1.0

2009.02.24



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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. This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

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