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Deep-Learning Based Joint Iris and Sclera Recognition with YOLO Network for Identity Identification

Chia-Wei Chuang and Chih-Peng Fan
Department of Electrical Engineering, National Chung Hsing University, Taiwan

Abstract—By jointly consideration of the partial iris and sclera region, no sclera and iris separation calculation is needed, and both of the sclera and iris information is used at the same time, and then the identity information is enhanced to avoid being forged. By the deep learning based YOLOv2 model, the visible-light eye images are marked with the jointly partial iris and sclera region, and the identity classifier is trained to inference the correct personal identity. By using the self-made and visible-light eye image database to evaluate the system performance, the proposed deep-learning based joint iris and sclera recognition reaches the mean Average Precision (mAP) up to 99%. Besides, compared with the previous works, the proposed design is more effective without using any iris and sclera segmentation process.
Index Terms—biometric, iris/sclera recognition, deep learning, YOLO model, personal identifications

Cite: Chia-Wei Chuang and Chih-Peng Fan, "Deep-Learning Based Joint Iris and Sclera Recognition with YOLO Network for Identity Identification," Journal of Advances in Information Technology, Vol. 12, No. 1, pp. 60-65, February 2021. doi: 10.12720/jait.12.1.60-65

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.