Pattern Recognition Using Hybrid Framework For Person Identification Retinal Iris Image Analysis

The human retina, particularly the iris, contains complex and unique patterns that remain stable over a lifetime, making it an exceptionally reliable biometric feature for person identification. Recognizing the growing demand for secure, efficient, and user-friendly biometric systems, this study introduces an innovative approach that integrates advanced computer vision techniques and deep learning architectures to achieve robust and scalable person identification through retinal iris image analysis. The proposed framework begins with the careful selection and collection of iris images from the CASIA dataset, focusing on a subset of ten individuals to ensure data diversity while maintaining experimental manageability