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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.
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Figure 1. Dimensionality reduction |
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A simple and effective source code for Face Recognition Based on Nonlinear PCA. |
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Demo code (protected
P-files) available for performance evaluation. Matlab Image Processing Toolbox is required.
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Release |
Date |
Major features |
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1.0 |
2009.02.24 |
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. This donation has to be considered an encouragement to improve the code itself. |
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Face Recognition Based on Nonlinear PCA. Click here for
your donation. In order to obtain the source code you
have to pay a little sum of money: 150 EUROS (less
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Once you have done this, please email us luigi.rosa@tiscali.it As soon as possible (in a few days) you will receive our new release of Face Recognition Based on Nonlinear PCA. Alternatively, you can bestow using our banking coordinates:
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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.
