Ahmad Nazri ALI, Mohd Zaid ABDULLAH, Shahrel Azmin SUANDI

Feature extraction using sequential cumulative bin and overlap mean intensity for iris classification

This paper examines an approach generalizing a variant of the local binary pattern (LBP) method for irisfeature extraction. The proposed method employs two different LBP variants called the sequential cumulative bin andoverlap mean intensity for projecting the one-dimensional local iris textures into a binary bit pattern. The assigned bit,either 1 or 0 as a bit code, replaces the original intensity value using a specific condition for the respective referenceelement. The ratio value from the total transition of 1 to 0 along the row axis represents the feature of each iris image.The extraction only utilizes a small area of interest on the iris image that covers parts of the iris textures with minimumeyelid and eyelashes. The assessment employs the support vector machines classifier and the result demonstrates a goodclassification rate with average accuracy of 94.0% for the individual mode. However, the classification rate has improvedto reach 96.5% accuracy if the assessment uses a concatenated mode set of features. Besides that, increasing the amountof samples in the training data by using the synthetic together with the original samples has also been able to improvethe classification rate.

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