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Source code for fingerprint recognition, face recognition and much more


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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.

 

 

 

 

 

Figure 1. Signature verification system



A simple and effective source code for Neural Networks Based Signature Recognition.

Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox, Matlab Neural Network Toolbox and Matlab Signal Processing Toolbox are required.

Release
Date
Major features
1.0

2008.12.15



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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.

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