OPTIMIZING IRIS RECOGNITION WITH ADVANCED MACHINE LEARNING METHODS

Abstract

Iris recognition is one of the most reliable and secure biometric authentication methods due to the uniqueness and stability of the iris pattern. However, to fully harness the potential of iris recognition systems, there is a need for robust and efficient algorithms that can accurately detect, match, and verify iris patterns under various conditions. The rise of machine learning techniques has significantly improved the performance of iris recognition systems, enabling them to handle challenges such as variations in lighting, occlusions, 
and differences in eye shapes. This paper explores advanced machine learning methods to optimize iris recognition, aiming to enhance both accuracy and speed.