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A major approach for iris recognition today is to generate feature vectors corresponding to individual iris images and to perform iris matching based on some distance metrics. One of the difficult problems in feature-based iris recognition is that the matching performance is significantly influenced by many parameters in feature extraction process, which may vary depending on environmental factors of image acquisition. We present an efficient algorithm for iris recognition using phase-based image matching. The use of phase components in 2D discrete Fourier transforms of iris images makes possible to achieve highly robust iris recognition in a unified fashion with a simple matching algorithm. Experimental evaluation using an iris image database clearly demonstrates an efficient matching performance of the proposed algorithm.

Index Terms: Matlab, source, code, iris recognition, iris matching, iris verification, phase, correlation, Fourier transforms.

 

 

 

 

 

Figure 1. Iris segmentation



A simple and effective source code for Iris Recognition Algorithm Using Phase-Based Image Matching. All tests were performed with CASIA Iris Image Database available at http://www.cbsr.ia.ac.cn/IrisDatabase.htm.

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

Release
Date
Major features
1.0

2007.08.06



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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 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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