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High information redundancy and correlation in face images result in
inefficiencies when such images are used directly for recognition. In
this paper, discrete cosine transforms are used to reduce image
information redundancy because only a subset of the transform
coefficients are necessary to preserve the most important facial
features such as hair outline, eyes and mouth. We demonstrate
experimentally that when DCT coefficients are fed into a backpropagation
neural network for classification, a high recognition rate can be
achieved by using a very small proportion of transform coefficients.
This makes DCT-based face recognition much faster than other
approaches.
Zhengjun Pan and Hamid Bolouri, "High Speed Face Recognition Based on
Discrete Cosine Transforms and Neural Networks", 1999.
Index Terms: Face recognition, neural networks, feature extraction, discrete cosine transform, face matching,
face identification, dct, ann, artificial neural networks, nn.
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Figure 1. Architecture of neural networks |
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A simple and effective source code for Face Identification based on DCT and Neural Networks. All tests were performed with AT&T face database available here. A complete list of public face databases is available at http://www.advancedsourcecode.com/facedatabase.asp. |
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Demo code (protected
P-files) available for performance evaluation. Matlab Image Processing Toolbox and Matlab Neural Network Toolbox are required. |
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Release |
Date |
Major features |
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1.0 |
2006.05.16 |
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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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DCT-ANN Based Face Recognition System - Release 1.0 - Click here for
your donation. In order to obtain the source code you
have to pay a little sum of money: 30 EUROS (less
than 42 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 DCT-ANN Based Face Recognition System. 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 14 SP1. Matlab Image Processing Toolbox and Matlab Neural Network Toolbox are 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.
