基于生成模型提升训练的深度学习虹膜识别方法

doi:10.13229/j.cnki.jdxbgxb20210438

Abstract

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

CLC Number:

TP391.41

Yuan-ning LIU,Lin ZHU,Xiao-dong ZHU,Zhen LIU,Hao-meng WU. Deep learning iris recognition method based on generative model boost training[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(12): 2924-2932.

Figures/Tables 12

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Table 1

Table 2

Table 3

References 32

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.

Related Articles 15

[1]	Xian-yu QI,Wei WANG,Lin WANG,Yu-fei ZHAO,Yan-peng DONG. Semantic topological map building with object semantic grid map [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 569-575.
[2]	Xiao-hu SHI,Jia-qi WU,Chun-guo WU,Shi CHENG,Xiao-hui WENG,Zhi-yong CHANG. Residual network based curve enhanced lane detection method [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 584-592.
[3]	Peng GUO,Wen-chao ZHAO,Kun LEI. Dual⁃resource constrained flexible job shop optimal scheduling based on an improved Jaya algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 480-487.
[4]	Jin-Zhen Liu,Guo-Hui Gao,Hui Xiong. Multi⁃scale attention network for brain tissue segmentation [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 576-583.
[5]	Jin-wu GAO,Zhi-huan JIA,Xiang-yang WANG,Hao XING. Degradation trend prediction of proton exchange membrane fuel cell based on PSO⁃LSTM [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(9): 2192-2202.
[6]	Feng-feng ZHOU,Hai-yang ZHU. SEE： sense EEG⁃based emotion algorithm via three⁃step feature selection strategy [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1834-1841.
[7]	Xuan-jing SHEN,Xue-feng ZHANG,Yu WANG,Yu-bo JIN. Multi⁃focus image fusion algorithm based on pixel⁃level convolutional neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1857-1864.
[8]	Fu-heng QU,Tian-yu DING,Yang LU,Yong YANG,Ya-ting HU. Fast image codeword search algorithm based on neighborhood similarity [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1865-1871.
[9]	Tian BAI,Ming-wei XU,Si-ming LIU,Ji-an ZHANG,Zhe WANG. Dispute focus identification of pleading text based on deep neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1872-1880.
[10]	Xiao-ying LI,Ming YANG,Rui QUAN,Bao-hua TAN. Unbalanced text classification method based on deep learning [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1889-1895.
[11]	Gui-he QIN,Jun-feng HUANG,Ming-hui SUN. Text input based on two⁃handed keyboard in virtual environment [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1881-1888.
[12]	Dan HU,Xin MENG. Vessel search method by earth observation satellite based on time⁃varying grid [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(8): 1896-1903.
[13]	Jun WANG,Yan-hui XU,Li LI. Data fusion privacy protection method with low energy consumption and integrity verification [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1657-1665.
[14]	Feng-feng ZHOU,Yi-chi ZHANG. Unsupervised feature engineering algorithm BioSAE based on sparse autoencoder [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1645-1656.
[15]	Ming-hua GAO,Can YANG. Traffic target detection method based on improved convolution neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1353-1361.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 10

[1]	Zhu Bing, Zhao Jian, Li Jing, Li You-de, Liu Wei, Wei Qing . Co-simulation platform with AMESim and MATLAB for traction control system[J]. 吉林大学学报(工学版), 2008, 38(增刊): 23 -0027 .
[2]	Ma Shun-li,Li Ming-zhe,Sun Gang,Li Xiang-ji,Qian Zhi-rui . Numerical simulation on multipoint forming process for tailorwelded blank[J]. 吉林大学学报(工学版), 2008, 38(02): 334 -0339 .
[3]	Tao Min, Li Jian-qiao, Yang Yin-sheng, Li Hong-wei, Pan Yan. Quantitative method for relationship between elements and properties of metalbased bionic drag reduction materials[J]. 吉林大学学报(工学版), 2006, 36(03): 367 -0370 .
[4]	Wang Jian, Ge An-lin,Lei Yu-long,Tian Hua . Three-dimensional design for hydraulic torque converter blades and its performance analysis[J]. 吉林大学学报(工学版), 2007, 37(01): 43 -47 .
[5]	LI Xiao-lei1,LI Dan,YIN Yong-guang. Enzymatic preparation of linear malto oligosaccharides[J]. 吉林大学学报(工学版), 2009, 39(06): 1559 -1562 .
[6]	SONG Chuan-Xue, GAO Jin, YANG Shu-Kai. Virtual simulation analysis about influence of suspension on full vehicle ride comfort[J]. 吉林大学学报(工学版), 2010, 40(增刊): 6 -00012 .
[7]	GUO Song, GU Guo-Chang, CA Ze-Su, LIU Hai-Bo, SHEN Jing. Face detection based on skin color segmentation and improved AdaBoostSVM algorithm[J]. 吉林大学学报(工学版), 2011, 41(02): 473 -0478 .
[8]	Song Yu-lai, Liu Yao-hui, Zhu Xian-yong, Wang Su-huan, Yu Si-rong. Effect of neodymium on microstructure and mechanical properties of AZ91 magnesium alloy[J]. 吉林大学学报(工学版), 2006, 36(03): 289 -0293 .
[9]	Lu Ying-rong，Yang Yin-sheng，Lv Feng . Optimal vehicle routing problem based on fuzzy clustering analysis[J]. 吉林大学学报(工学版), 2006, 36(增刊2): 147 -151 .
[10]	ZHANG Ying-feng, MA Biao, ZHU Yuan, ZHANG Jin-le. State identification of power-shift steering transmission based on hypersphere support vector machine[J]. 吉林大学学报(工学版), 2012, 42(01): 13 -18 .

分类模型	CASIA-Iris-Thousand		JLU6.0
分类模型	CRR	EER	CRR	EER
VGG16	79.46	3.37	90.95	4.50
VGG16-Pretrained	94.33	1.35	96.31	2.42
VGG16+Data Augment	93.29	1.07	96.70	2.38
VGG16-EnhanceDeepIris	94.97	1.06	96.67	1.63

分类模型	CASIA-Iris-Thousand		JLU6.0
分类模型	CRR	EER	CRR	EER
ResNet101	86.20	2.06	89.73	5.89
ResNet101-Pretrained	97.37	1.20	97.38	0.93
ResNet101+Data Augment	97.26	0.63	98.87	0.29
ResNet101-EnhanceDeepIris	98.65	0.63	99.82	0.22

分类模型	CASIA-Iris-Thousand		JLU6.0
分类模型	CRR	EER	CRR	EER
DenseNet121	91.09	1.47	89.79	5.36
DenseNet121-Pretrained	97.13	0.93	96.99	0.59
DenseNet121+Data Augment	97.78	0.70	98.93	0.59
DenseNet121-EnhanceDeepIris	98.88	0.57	99.46	0.18

Deep learning iris recognition method based on generative model boost training

RICH HTML

PDF (PC)