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

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