Iris recognition system using morphology and sequential addition based grouping

Abstract

Iris recognition is one of the most reliable and efficient methods for biometric identification because of its richness in texture information. The proposed method is based on Morphology and Sequential addition based grouping that reduces the complexity and improves the performance of the Iris recognition system. In this method, pupil localization is done using negative function and four neighbours so that any type of pupil boundary, either circular or ellipse, is detected accurately. Then, Morphology and Region of interest (ROI) extraction is done for Iris localization in order to isolate the useful iris regions without eyelashes and other occlusions. Furthermore, the resultant iris portion is transformed into polar coordinates system for normalization process using Daugman's rubber sheet model. Histogram equalization is applied for enhancing the normalized iris image. Finally, feature extraction and matching is performed using Sequential Addition-based grouping and hamming distance approach. The Chinese Academy of Sciences-institute of Automation (CASIA) database is used to stimulate the studies. The proposed algorithm reduced the computational time and increased the recognition accuracy to a great extent as compared with existing algorithms.