Segmentation Approach for a Noisy Iris Images Based on Block Statistical Parameters

Abstract

The Iris localization plays a big role in the performance of an iris recognition system. This is due to the dependent of the next steps up on it, and the incorrect segmentation might lead to inexact normalization and improper feature extraction from less discriminatory parts (eyelids, eyelashes, pupil, etc.) so the execution of system will diminish. An effective method for locating the iris of the eye is suggested in this paper. At first a mixture of gamma transform and contrast enhancing mechanisms are used to guarantee a precise renovation of eye image to become an iris area easy to isolate. The next step is relayed on calculating the statistical image parameters (i.e., the mean and standard deviation) which are employed as a feature to detect outer iris boundary. The integro-differential operational technique is used with further pre-processing processes to detect the inner boundaries of iris. The noisy iris UBIRIS.v1 dataset was used in the experiment. Thee conducted results indicated that the proposed technique has a good performance, which is improved accuracy of iris localization step for noisy dataset.