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There has been a rapid increase in the need of accurate and reliable personal identification infrastructure in recent years, and biometrics has become an important technology for the security. Iris recognition has been considered as one of the most reliable biometrics technologies in recent years. The human iris is the most important biometric feature candidate, which can be used for differentiating the individuals. For systems based on high quality imaging, a human iris has an extraordinary amount of unique details. Features extracted from the human iris can be used to identify individuals, even among genetically identical twins. Iris-based recognition system can be noninvasive to the users since the iris is an internal organ as well as externally visible, which is of great importance for the real-time applications.

We have developed an iris recognition method based on genetic algorithms (GA) for the optimal features extraction. The accurate iris patterns classification has become a challenging issue due to the huge number of textural features extracted from an iris image with comparatively a small number of samples per subject. The traditional feature selection schemes like principal component analysis, independent component analysis, singular valued decomposition etc. require sufficient number of samples per subject to select the most representative features sequence; however, it is not always realistic to accumulate a large number of samples due to some security issues. We propose GA to improve the feature selection by optimal filtering.

This code is based on Libor Masek's excellent implementation available here.

Libor Masek, Peter Kovesi. MATLAB Source Code for a Biometric Identification System Based on Iris Patterns. The School of Computer Science and Software Engineering, The University of Western Australia, 2003.

All tests were performed with CASIA Iris Image Database available at http://www.cbsr.ia.ac.cn/IrisDatabase.htm.

Index Terms: Matlab, source, code, iris, recognition, matching, GA, genetic, algorithms, algorithm.

 

 

 

 

 

Figure 1. Genetic sequence



A simple and effective source code for Iris Recognition Based on Genetic Algorithms.



Demo code (protected P-files) available for performance evaluation. Matlab Image Processing Toolbox is required.

Release
Date
Major features
1.0

2010.05.05



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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.

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