吉林大学学报(工学版) ›› 2022, Vol. 52 ›› Issue (12): 2924-2932.doi: 10.13229/j.cnki.jdxbgxb20210438

• 计算机科学与技术 • 上一篇    下一篇

基于生成模型提升训练的深度学习虹膜识别方法

刘元宁1,2(),朱琳1,2,朱晓冬1,2(),刘震1,3,吴浩萌1   

  1. 1.吉林大学 计算机科学与技术学院，长春 130012
    2.吉林大学 符号计算与知识工程教育部重点实验室，长春 130012
    3.长崎综合科学大学 研究生院工学研究科，长崎 851-0193
  • 收稿日期:2021-05-17 出版日期:2022-12-01 发布日期:2022-12-08
  • 通讯作者: 朱晓冬 E-mail:lyn@jlu.edu.cn;zhuxd@jlu.edu.cn
  • 作者简介:刘元宁（1962-），男，教授，博士生导师. 研究方向：虹膜识别. E-mail：lyn@jlu.edu.cn
  • 基金资助:
    吉林省自然科学基金项目(YDZJ202101ZYTS144);国家自然科学基金项目(61471181)

Deep learning iris recognition method based on generative model boost training

Yuan-ning LIU1,2(),Lin ZHU1,2,Xiao-dong ZHU1,2(),Zhen LIU1,3,Hao-meng WU1   

  1. 1.College of Computer Science and Technology，Jilin University，Changchun 130012，China
    2.Key Laboratory of Symbolic Computation and Knowledge Engineering，Ministry of Education，Jilin University，Changchun 130012，China
    3.Graduate School of Engineering，Nagasaki Institute of Applied Science，Nagasaki 851-0193，Japan
  • Received:2021-05-17 Online:2022-12-01 Published:2022-12-08
  • Contact: Xiao-dong ZHU E-mail:lyn@jlu.edu.cn;zhuxd@jlu.edu.cn

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摘要：

提出了一种改进的深度虹膜分类模型EnhanceDeepIris，在生成网络的辅助下，对深度学习虹膜分类网络进行二次训练，使已经在原始训练集上收敛的分类网络继续训练，得到在测试集上泛化能力更好的网络。使用3个先进的图像分类网络VGG16、ResNet101和DenseNet121验证EnhanceDeepIris对深度学习分类网络的提升效果。在两个虹膜数据集CASIA-Iris-Thousand和JLU6.0上对该方法进行实验，结果表明，与传统数据增强方法相比，经过EnhanceDeepIris提升训练的分类模型识别精度更高、测试效果更稳定。

关键词: 计算机应用, 深度学习, 虹膜识别, 图像生成, 辅助分类

Abstract:

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.

Key words: computer application, deep learning, iris recognition, image generation, auxiliary classification

中图分类号: 

  • TP391.41

图1

提升虹膜识别方法的框架"

图2

虹膜图像预处理"

图3

统一归一化图像的长宽"

图4

EnhanceDeepIris模型架构"

图5

采样图像"

图6

一幅虹膜图像及其增强图像"

图7

模型输入、输出图像"

图8

不同模型在CASIA-Iris-Thousand数据集和JLU6数据集上的正确识别率"

图9

CASIA-Iris-Thousand数据集和JLU6.0数据集的ROC曲线"

表1

不同虹膜库在VGG16系列模型上的匹配结果 (%)"

分类模型CASIA-Iris-ThousandJLU6.0
CRREERCRREER
VGG1679.463.3790.954.50
VGG16-Pretrained94.331.3596.312.42
VGG16+Data Augment93.291.0796.702.38
VGG16-EnhanceDeepIris94.971.0696.671.63

表2

不同虹膜库在ResNet101系列模型上的匹配结果 (%)"

分类模型CASIA-Iris-ThousandJLU6.0
CRREERCRREER
ResNet10186.202.0689.735.89
ResNet101-Pretrained97.371.2097.380.93
ResNet101+Data Augment97.260.6398.870.29
ResNet101-EnhanceDeepIris98.650.6399.820.22

表3

不同虹膜库在DenseNet121系列模型上的匹配结果 (%)"

分类模型CASIA-Iris-ThousandJLU6.0
CRREERCRREER
DenseNet12191.091.4789.795.36
DenseNet121-Pretrained97.130.9396.990.59
DenseNet121+Data Augment97.780.7098.930.59
DenseNet121-EnhanceDeepIris98.880.5799.460.18
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