Segmentation and Detection of Iris Region for Noisy Regions Taken in Unconstrained Using Fuzzy Weightage Membership Function and Hough Transform

Abstract — For more strict security requirements, biometric research has experienced significant advances in recent years. More important is the need to overcome the rigid constraints necessitated by the practical implementation of sensible but effective security methods such as recognition. An inventive iris acquisition method with less constraints on the iris verification and identification process as well as on the subject. Consequently, to provide acceptable measures of accuracy, it is critical for such an iris recognition system to be complemented by a robust iris segmentation approach to overcome various noise effects introduced through image capture under different recording environments and scenrarios. The proposed algorithm consists of few steps of detecting the approximate localization of the eye area of the noisy image captured at the visible wavelength using the extracted sclera area, defining the outer iris boundary which is the boundary between iris and sclera detecting the upper and lower eyelids, conducting the verification and correction for outer iris boundary detection and detecting the pupil area and eyelashes and conducting the verification of the reliability of the segmentation results. Experimental results clear that it gives better segmentation of iris region and better detecting of upper and lower eyelids which result in valid iris region detection. This can be implemented in real time application due to less time taken by the algorithm in segmentation