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The local binary pattern (LBP) operator is defined as a gray-scale invariant texture measure, derived from a general definition of texture in a local neighborhood. Through its recent extensions, the LBP operator has been made into a really powerful measure of image texture, showing excellent results in many empirical studies. The LBP operator can be seen as a unifying approach to the traditionally divergent statistical and structural models of texture analysis. Perhaps the most important property of the LBP operator in real-world applications is its invariance against monotonic gray level changes. Another equally important is its computational simplicity, which makes it possible to analyze images in challenging real-time settings. The LBP method and its variants have already been used in a large number of applications all over the world. The LBP is a non-parametric kernel which summarizes the local spacial structure of an image. Moreover, it is invariant to monotonic gray-scale transformations, hence the LBP representation may be less sensitive to changes in illumination. This is a very interesting property in iris recognition. Iris-based personal identification has attracted much attention in recent years. Almost all the state-of-the-art iris recognition algorithms are based on statistical classifier and local image features, which are noise sensitive and hardly to deliver perfect recognition performance. We have developed a novel iris recognition method, using the histogram of local binary pattern for global iris texture representation and classification.

Index Terms: Matlab, source, code, iris, recognition, segmentation, detection, verification, matching, lbp, local, binary, pattern, histogram.

 

 

 

 

 

Figure 1. Local binary pattern histogram



A simple and effective source code for Iris Recognition Based On Local Binary Pattern.

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

Release
Date
Major features
1.0

2009.02.03



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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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