Abstract—In this study, the proposed iris recognition method uses the You Only Look Once (YOLO)-based deep learning algorithm with the procedure divided into two stages. After extraction of the iris and pupil from the images, the iris Region of Interest (ROI) is identified by the classifier. Iris localization, iris segmentation, and feature enhancement are three crucial processes when extracting the iris ROI, and they constitute the first stage. Iris localization is firstly discussed, and the three methods are proposed with the system performance analyzed from the perspective of both system safety and affordability. The main difference among these methods is their complexity. Iris segmentation is then introduced, and an experiment is conducted to evaluate system performance when images are preprocessed for inputs by different segmentation methods, including images with and without normalization. Normalization and its necessary or unnecessary role in identifying images with deep learning are then analyzed. Finally, an examination of how feature enhancement influences the results of the proposed method is outlined. For system safety analysis, the Equal Error Rate (EER) of the proposed design approaches near zero; for system affordability analysis, the accuracy of the proposed design can be up to 98%.
 
Keywords—biometric recognition, iris identification, iris location, YOLOv4-tiny, deep learning


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