The Iris Normalization Method Based on Line

Guo, Qiaoli; Cao, Hongju; Dong, Benqing; Xiang, Zheng

doi:10.1109/isdea.2013.559

Cited by 2 publications

(2 citation statements)

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“…The workflow of most traditional iris recognition systems [3][4][5][6][7][8][9][10][11][12] can be split into the following steps: iris localization, segmentation, normalization, feature enhancement, feature extraction, and identification. To increase system performance, evaluation index estimation and more complex methods were proposed.…”

Section: A Iris Recognition Systemmentioning

confidence: 99%

“…Daugman proposed the rubber sheet model to normalize iris information with polar coordinates following iris segmentation. Many researchers have reported various methods of iris normalization based on Daugman's model, such as Qiaoli et al [8] and Mohammed et al [9]. To improve system accuracy, related images are being often further preprocessed after iris normalization.…”

Section: Introductionmentioning

confidence: 99%

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Two-Stage Deep Learning Technology Based Iris Recognition Methodology for Biometric Authorization

Hsiao,

Chang,

Fan

2024

JAIT

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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%.

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