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
1 Wildes R P. Iris recognition: an emerging biometric technology[J]. Proceedings of the IEEE, 1997, 85(9): 1348-1363.
2 Daugman J G. High confidence visual recognition of persons by a test of statistical independence[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1993, 15(11): 1148-1161.
3 Boles W W, Boashash B. A human identification technique using images of the iris and wavelet transform[J]. IEEE Transactions on Signal Processing, 1998, 46(4): 1185-1188.
4 Tian Qi-chuan, Pan Quan, Cheng Yong-mei, et al. Iris feature extracting algorithm based on zero-crossing detection[J]. Journal of Electronics and Information Technology, 2006, 28(8): 1452-1457.
5 Hollingsworth K P, Bowyer K W, Flynn P J. Improved iris recognition through fusion of hamming distance and fragile bit distance(article)[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2465-2476.
6 刘元宁,刘帅,朱晓冬,等. 基于高斯拉普拉斯算子与自适应优化伽柏滤波的虹膜识别[J]. 吉林大学学报:工学版, 2018, 48(5): 1606-1613.
Liu Yuan-ning, Liu Shuai, Zhu Xiao-dong, et al. Log operator and adaptive optimization gabor filtering for iris recognition[J]. Journal of Jilin University (Engineering and Technology Edition), 2018, 48(5): 1606-1613.
7 刘元宁,刘帅,朱晓冬,等. 基于决策粒子群优化与稳定纹理的虹膜二次识别[J]. 吉林大学学报:工学版, 2019, 49(4): 1329-1338.
Liu Yuan-ning, Liu Shuai, Zhu Xiao-dong, et al. Iris secondary recognition based on decision particle swarm optimization and stable texture[J]. Journal of Jilin University (Engineering and Technology Edition), 2019, 49(4): 1329-1338.
8 Russakovsky O, Deng J, Su H, et al. Imagenet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115(3): 211-252.
9 Krizhevsky A, Sutskever I, Hinton G. Imagenet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.
10 Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J/OL]. [2021-04-25].
11 He Kai-ming, Zhang Xiang-yu, Ren Shao-qing, et al. Deep residual learning for image recognition[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016: 770-778.
12 Huang G, Liu Z, van Der Maaten L, et al. Densely connected convolutional networks[C]∥IEEE Coference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 2017: 2261-2269.
13 Szegedy C, Liu W, Jia Y Q, et al. Going deeper with convolutions[C]∥Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, 2015: 1-9.
14 Liu N, Zhang M, Li H, et al. DeepIris: learning pairwise filter bank for heterogeneous iris verification[J]. Pattern Recognition Letters, 2015, 82(2): 154-161.
15 Gangwar A, Joshi A. Deepirisnet: deep iris representation with applications in iris recognition and cross-sensor iris recognition[C]∥IEEE International Conference on Image Processing, Phoenix, AZ, USA, 2016: 2301-2305.
16 Al-Waisy A S, Qahwaji R, Ipson S, et al. A multi-biometric iris recognition system based on a deep learning approach[J]. Pattern Analysis & Applications, 2018, 21(3): 783-802.
17 Zhao T, Liu Y, Huo G, et al. A deep learning iris recognition method based on capsule network architecture[J]. IEEE Access, 2019(7): 49691-49701.
18 Nguyen K, Fookes C, Ross A, et al. Iris recognition with off-the-shelf CNN features: a deep learning perspective[J]. IEEE Access, 2018(6): 18848-18855.
19 Goodfellow I J, Pouget-Abadie J, Mirza M, et al. generative adversarial networks[J]. Advances in Neural Information Processing Systems, 2014(3): 2672-2680.
20 邹秀芳, 朱定局. 生成对抗网络研究综述[J]. 计算机系统应用, 2019, 28(11): 5-13.
Zou Xiu-fang, Zhu Ding-ju. Review of generative countermeasure network[J]. Computer Systems Applications, 2019, 28(11): 5-13
21 Zhu J Y, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]∥Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017: 2242-2251.
22 Wang T C, Liu M Y, Zhu J Y, et al. High-resolution image synthesis and semantic manipulation with conditional GANS[C]∥Proceedings of the IEEE Computer Society Conference on Computer Vision & Pattern Recognition(CVPR), Salt Lake City, UT, USA, 2018: 8798-8807.
23 Liu G L, Reda F A, Shih K J, et al. Image inpainting for irregular holes using partial convolutions[C]∥European Conference on Computer Vision(ECCV), Munich, Germany, 2018: 89-105.
24 陈英, 刘志鑫. 基于GAN的虹膜数据增强方法研究[J]. 信息通信, 2020, 214(10): 36-40.
Chen Ying, Liu Zhi-xin. Research on iris data enhancement method based on GAN[J]. Information & Communications, 2020, 214(10): 36-40.
25 Waheed A, Goyal M, Gupta D, et al. Covid GAN: Data augmentation using auxiliary classifier GAN for improved covid-19 detection[J]. IEEE Access, 2020(8): 91916-91923.
26 Chinese academy of sciences institute of automation. CASIA iris image database[DB/OL]. [2021-04-30].
27 Othman N, Dorizzi B, Garcia-Salicetti S. OSIRIS: an open source iris recognition software[J]. Pattern Recognition Letters, 2016(82): 124-131.
28 程广涛, 巩家昌, 赵洪伟. 基于膨胀卷积和稠密连接的烟雾识别方法[J]. 计算机工程, 2020, 46(4): 253-259.
Cheng Guang-tao, Gong Jia-chang, Zhao Hong-wei. Smoke recognition method based on dilated convolution and dense connection[J]. Computer Engineering, 2020, 46(4): 253-259.
29 袁自勇, 高曙, 曹姣, 等. 基于异构图卷积网络的小样本短文本分类方法[J]. 计算机工程, 2021, 47(12): 87-94.
Yuan Zi-yong, Gao Shu, Cao Jiao, et al. Method for few-shot short text classification based on heterogeneous graph convolutional network[J]. Computer Engineering, 2021, 47(12): 87-94.
30 袁梅, 全太锋, 黄俊, 等. 基于卷积神经网络的烟雾检测[J]. 重庆邮电大学学报: 自然科学版, 2020, 32(4): 620-629.
Yuan Mei, Quan Tai-feng, Huang Jun, et al. Smoke detection based on convolutional neural network[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2020, 32(4): 620-629.
31 何欣, 李书琴, 刘斌. 基于多尺度残差神经网络的葡萄叶片病害识别[J]. 计算机工程, 2021, 47(5): 285-291, 300.
He Xin, Li Shu-qin, Liu Bin. Identification of grape leaf diseases based on multi-scale residual neural network[J]. Computer Engineering, 2021, 47(5): 285-291, 300.
32 李新春,马红艳,林森.基于局部邻域四值模式的掌纹掌脉融合识别[J].重庆邮电大学学报: 自然科学版, 2020, 32(4): 630-638.
Li Xin-chun, Ma Hong-yan, Lin Sen. Palmprint and palm vein fusion recognition based on local neighbor quaternary pattern[J]. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 2021, 47(5): 285-291, 300.
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