Robust Iris Recognition via Annotated Segmentation and SIFT Feature Extraction on the CASIA-Iris-Thousand Dataset

Iris recognition remains one of the most accurate biometric methods for personal identification, especially in high-security environments. In this work, we investigate the use of the ScaleInvariant Feature Transform (SIFT) algorithm to extract distinctive features from iris images. Using the CASIA-Iris-Thousand dataset, we conduct preprocessing to enhance image clarity, annotate iris regions using the LabelMe tool, generate precise masks, and apply SIFT to extract robust keypoints and descriptors. The aim is to assess whether traditional computer vision techniques like SIFT are suitable for iris feature extraction, especially when combined with careful preprocessing and region annotation. Results indicate that SIFT, when applied to accurately masked iris regions, can effectively highlight keypoint-based features for further biometric analysis.