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Signature verification technology utilizes the distinctive aspects of the
signature to verify the identity of individuals. The technology examines the
behavioral components of the signature, such as stroke order, speed and pressure,
as opposed to comparing visual images of signatures. Unlike traditional signature
comparison technologies, signature verification measures the physical activity of signing.
While a system may also leverage a comparison of the visual appearance of a signature,
or "static signature," the primary components of signature verification
are behavioral. In the last few decades, many approaches have been developed in the pattern
recognition area, which approached the off-line signature verification problem.
There are two main approaches for off-line signature verification: static
approaches and pseudodynamic approaches. The first one involves perceptive
characteristics, therefore easy to imitate. The second involves imperceptive
characteristics, therefore difficult to imitate.
As for the verification process, there are many approaches that are used
nowadays, for example, Hidden Markov Models, the Euclidean Distance Classifiers,
Elastic Image Matching and others. Neural Networks have, in the last
decade, attracted the attention of many researchers in the pattern recognition area, for
example the recognition of handwritten text, speech recognition and recently
the verification of on-line signatures. These models have the capacity to
absorb the variability between patterns and their similarity.
Code has been tested using Off line signature database, Grupo de Procesado Digital de Señales,
available at http://www.gpds.ulpgc.es/download/index.htm.
Index Terms: Matlab, source, code, signature, on-line, off-line, verification, matching, ann, nn, neural, network, networks.
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Figure 1. Signature verification system |
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A simple and effective source code for Neural Networks Based Signature Recognition. |
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Demo code (protected
P-files) available for performance evaluation. Matlab Image Processing Toolbox, Matlab Neural Network Toolbox and Matlab Signal Processing Toolbox are required. |
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Release |
Date |
Major features |
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1.0 |
2008.12.15 |
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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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Neural Networks Based Signature Recognition - Click here for
your donation. In order to obtain the source code you
have to pay a little sum of money: 28 EUROS (less
than 40 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 Neural Networks Based Signature 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, Matlab Neural Network Toolbox and Matlab Signal Processing 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.
