Is normalized iris optimal for iris recognition based on deep learning?

被引：3

作者：

Jia, Dingding ^{[1
]}

Shen, Wenzhong ^{[1
]}

机构：

[1] Shanghai Univ Elect Power, Sch Elect & Informat Engn, Shanghai, Peoples R China

来源：

JOURNAL OF ELECTRONIC IMAGING | 2021年 / 30卷 / 05期

关键词：

iris recognition; neural network; network input; iris normalization; visualization;

D O I：

10.1117/1.JEI.30.5.053007

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The input of the iris classification network based on deep learning has two forms: one is the coarsely located iris region of interest; the other is the normalized iris. To solve the problem of whether it is necessary to normalize the iris, experiments are carried out on the above two input forms, and the results show that the iris normalization is still the best choice. To adapt to the visual characteristics of the neural network, an iris normalization processing method is proposed: starting from 90 deg, the iris circle is mapped to polar coordinates and the normalized rectangle is cropped, rotated, and spliced. Experimental and visualization results show that the proposed iris normalization strategy has better results of iris recognition than other normalization methods. (C) 2021 SPIE and IS&T

引用

页数：14

共 50 条

[21] Deep Learning for Robust Iris Recognition: Introducing Synchronized Spatiotemporal Linear Discriminant Model-Iris [J].

Kadhim, Saif ;

Paw, Ko Siaw ;

Tak, Yaw. Chong ;

Ameen, Shahad ;

Alkhayyat, Ahmed .

ADVANCES IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING, 2025, 5 (01) :3446-3464

[22] Iris Recognition Based on Characters of Iris's Speckles [J].

Liang, Huaqing ;

Cai, Zhiming ;

Chen, Xiuxin ;

Shuang, Kai .

2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23, 2008, :6793-6797

[23] Iris Location and Recognition by Deep-Learning Networks Based Design for Biometric Authorization [J].

Hsiao, Cheng-Shun ;

Fan, Chih-Peng ;

Hwang, Yin-Tsung .

2021 IEEE 3RD GLOBAL CONFERENCE ON LIFE SCIENCES AND TECHNOLOGIES (IEEE LIFETECH 2021), 2021, :144-145

[24] Post-mortem iris recognition with deep-learning-based image segmentation [J].

Trokielewicz, Mateusz ;

Czajka, Adam ;

Maciejewicz, Piotr .

IMAGE AND VISION COMPUTING, 2020, 94 (94)

[25] Iris recognition based on few-shot learning [J].

Lei, Songze ;

Dong, Baihua ;

Li, Yonggang ;

Xiao, Feng ;

Tian, Feng .

COMPUTER ANIMATION AND VIRTUAL WORLDS, 2021, 32 (3-4)

[26] Deep metric learning method for open-set iris recognition [J].

Huo, Guang ;

Li, Ruyuan ;

Lou, Jianlou ;

Yu, Xiaolu ;

Wang, Jiajun ;

He, Xinlei ;

Wang, Yue .

JOURNAL OF ELECTRONIC IMAGING, 2024, 33 (03) :33016

[27] Efficient iris recognition system based on iris anatomical structure [J].

Xiaoyan Yuan ;

Pengfei Shi .

IEICE ELECTRONICS EXPRESS, 2007, 4 (17) :555-560

[28] Iris recognition based on robust iris segmentation and image enhancement [J].

Verma, Abhishek ;

Liu, Chengjun ;

Jia, Jiancheng .

INTERNATIONAL JOURNAL OF BIOMETRICS, 2012, 4 (01) :56-76

[29] Iris recognition based on sectors [J].

Yang S. ;

Wu X. ;

Karn P. ;

He X. .

Wu, Xiaohong (wxh@scu.edu.cn), 1600, Sichuan University (48) :150-156

[30] Iris recognition based on FFG [J].

Kim, Hee-Sung ;

Bae, Byung-Kyu ;

Cho, Jun-Hee .

IECON 2007: 33RD ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1-3, CONFERENCE PROCEEDINGS, 2007, :2454-2458

← 1 2 3 4 5 →