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In a task such as face recognition, much of the important information may be contained in the high-order relationships
among the image pixels. A number of face recognition algorithms employ principal component analysis (PCA), which
is based on the second-order statistics of the image set, and does not address high-order statistical dependencies such
as the relationships among three or more pixels. Independent component analysis (ICA) is a generalization of PCA
which separates the high-order moments of the input in addition to the second-order moments. ICA was performed
on a set of face images by an unsupervised learning algorithm derived from the principle of optimal information
transfer through sigmoidal neurons. The algorithm maximizes the mutual information between the input and the
output, which produces statistically independent outputs under certain conditions. ICA representation was superior to
representations based on principal components analysis for recognizing faces across sessions and changes in expression.
Index Terms: Matlab, source, code, face, recognition, ICA, independent, component, analysis.
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Figure 1. ICA decomposition |
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A simple and effective source code for ICA Face Recognition. |
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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 |
2012.02.17 |
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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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ICA Face Recognition - Click here for
your donation. In order to obtain the source code you
have to pay a little sum of money: 40 EUROS (less
than 56 U.S. Dollars). |
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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 ICA Face Recognition. 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.
