Iris Detection and Reorganization System Using Classification Algorithm in Machine Learning

Abstract

Iris detection and recognition are critical components in biometric identification systems, offering high accuracy and reliability. This study presents a comprehensive approach to iris detection and recognition using advanced classification algorithms in machine learning, applied to the FRGC dataset. Initially, eye detection is performed using the Viola-Jones face detection method, ensuring robust and rapid identification of the eye region. Following this, iris segmentation is achieved through the Hough Transform, effectively isolating the iris from the sclera utilizing Canny edge detection for precise boundary delineation. To discriminate and classify the intricate texture patterns of the iris, a Convolutional Neural Network (CNN) is employed, leveraging its powerful feature extraction and classification capabilities. By combining these approaches, a high-performance iris recognition system is guaranteed, exhibiting notable gains in processing speed and accuracy. The efficiency of the suggested method is confirmed by experimental results, which also show that it has the potential to be used in practical biometric applications. The work opens the door for upcoming developments in biometric identification technology by highlighting the complementary nature of contemporary machine learning techniques and traditional image processing methods.