Design and Analysis of Deep-Learning Based Iris Recognition Technologies by Combination of U-Net and EfficientNet

被引：11

作者：

Hsiao, Cheng-Shun ^{[1
]}

Fan, Chih-Peng ^{[1
]}

Hwang, Yin-Tsung ^{[1
]}

机构：

[1] Natl Chung Hsing Univ, Dept Elect Engn, Taichung, Taiwan

来源：

2021 9TH INTERNATIONAL CONFERENCE ON INFORMATION AND EDUCATION TECHNOLOGY (ICIET 2021) | 2021年

关键词：

deep learning; U-Net; EfficientNet; iris recognition; biometric authentication;

D O I：

10.1109/ICIET51873.2021.9419589

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, the effective deep-learning based methodology is developed for iris biometric authentication. Firstly, based on the U-Net model, the proposed system uses the semantic segmentation technology to localize and extract the region of interest (ROI) of iris. After the ROI of iris in the eye image is revealed, the inputted eye image will be cropped to the small-size eye image with the just-fitted ROI of iris. Then, the iris features of the cropped eye image are strengthened optionally by adaptive histogram equalization or Gabor filtering process. Finally, the cropped iris image is classified by the EfficientNet model. By the Chinese Academy of Sciences Institute of Automation (CASIA) v1 database, the proposed deep-learning based iris recognition scheme reaches the recognition accuracies up to 98%. Compared with the previous works, the proposed technology can provide the effective iris recognition accuracy for the biometrics applications with iris information.

引用

页码：433 / 437

页数：5

共 18 条

[1]

Arora S, 2018, IEEE INT ADV COMPUT, P157, DOI 10.1109/IADCC.2018.8692114

[2]

Chinese Academy of Sciences Institute of Automation, 2017, CASIA IRIS IMAGE DAT

[3] How iris recognition works [J].

Daugman, J .

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2004, 14 (01) :21-30

[4] HIGH CONFIDENCE VISUAL RECOGNITION OF PERSONS BY A TEST OF STATISTICAL INDEPENDENCE [J].

DAUGMAN, JG .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1993, 15 (11) :1148-1161

[5]

Liu SY, 2015, PROCEEDINGS 3RD IAPR ASIAN CONFERENCE ON PATTERN RECOGNITION ACPR 2015, P730, DOI 10.1109/ACPR.2015.7486599

[6]

Mehrotra Hunny, 2011, 8 INT C INF COMM SIG

[7]

Mingxing Tan, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Proceedings, P10778, DOI 10.1109/CVPR42600.2020.01079

[8]

Nadia Feddaoui, 2009, INT MULTICONFERENCE

[9]

Nakashima Y, 2017, I S INTELL SIG PROC, P275, DOI 10.1109/ISPACS.2017.8266488

[10] U-Net: Convolutional Networks for Biomedical Image Segmentation [J].

Ronneberger, Olaf ;

Fischer, Philipp ;

Brox, Thomas .

MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION, PT III, 2015, 9351 :234-241

← 1 2 →