Iris Recognition Method for Non-cooperative Images

When iris images are collected under optimal circumstances, traditional iris segmentation algorithms provide accurate findings. However, an iris identification system’s success is heavily dependent on the precision with which it segments iris pictures, particularly when dealing with irises that are non-cooperative. This research investigates the challenge of recognizing irises in low-quality photos taken under challenging lighting and other imaging situations. In order to reduce processing time and eliminate noise caused by eyelashes and eyelids, the system first acquires an iris image, then improves the image quality, detects the iris boundary and the pupil, detects the eyelids, removes the eyelashes and the shadows, and converts the iris coordinates from Cartesian to polar coordinates. The iris's characteristics are extracted with the use of a Gabor filter and then compared with the help of Euclidean distance. After using the suggested technique, we compared the outcomes with those found in the literature and found that the proposed method yields significant improvements in segmentation accuracy and recognition performance.