Implementation of Enhanced Iris Recognition for Secure Biometric Authentication using Machine Learning

Developing effective strategies to address significant variations in segmented iris image quality is crucial for accurate iris recognition from remotely acquired facial or eye images. These variations are closely linked to the consistency of iris feature encoding, suggesting that leveraging such discrepancies can improve matching accuracy. A nonlinear approach is recommended for considering both the local consistency of the iris pattern and the overall quality of the weight map. Principal Component Analysis (PCA) and Support Vector Machines (SVM) are commonly employed in biometric research for iris recognition, identifying individuals through unique iris features. This research aims to enhance the precision and efficiency of iris recognition systems through methods such as feature extraction, classifier algorithms, integration of multiple biometric modalities, and the development of comprehensive and diverse iris datasets. Iris recognition technology offers significant potential to bolster security and privacy in applications including access control, border security, and financial transactions.