Deep learning iris recognition method based on generative model boost training

被引：0
|
作者：
Liu Y.-N. [1 ,2 ]
Zhu L. [1 ,2 ]
Zhu X.-D. [1 ,2 ]
Liu Z. [1 ,3 ]
Wu H.-M. [1 ]
机构：
[1] College of Computer Science and Technology, Jilin University, Changchun
[2] Key Laboratory of Symbolic Computation and Knowledge Engineering, Ministry of Education, Jilin University, Changchun
[3] Graduate School of Engineering, Nagasaki Institute of Applied Science, Nagasaki
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2022年 / 52卷 / 12期
关键词：
auxiliary classification; computer application; deep learning; image generation; iris recognition;
D O I：
10.13229/j.cnki.jdxbgxb20210438
中图分类号：
学科分类号：
摘要：
An enhanced deep iris classification model EnhanceDeepIris was proposed，with the help of generating network，second trains a iris classification network of deep learning which has already converged on the original training set，to make it can continue be trained and get better generalization ability on the test set. Three most advanced image classification networks VGG16，ResNet101 and DenseNet121 were used to verify the improvement effect of EnhanceDeepIris on deep learning classification networks. The method was tested on two iris datasets CASIA-Iris-Thousand and JLU6.0. Compared with the traditional data augment method，the classification model trained by EnhanceDeepIris has higher correct recognition rate and more stable test effect. © 2022 Editorial Board of Jilin University. All rights reserved.
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页码：2924 / 2932
页数：8
相关论文
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