Skip to main content
SpringerLink
Log in

Data-knowledge driven: a new learning strategy for iris recognition

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This article focuses on the issues of poor interpretability and low universality of traditional iris recognition models in unsteady states. It proposes a new learning strategy for iris recognition: data-knowledge driven strategy, whose core idea is that the iris category knowledge is extracted from the clustering range of the iris feature data, and the knowledge is integrated into the recognition decision-making process to promote the recognition. The process of knowledge cluster analysis enables users to clearly understand the process of obtaining decision basis, and improves the interpretability of the process of recognition model design. The iris feature knowledge is set according to the consistent fact reflected in the data distribution of a large number of iris samples in various scenarios under the same process, which enhances the universality of the iris recognition model in the unsteady state. In addition, the data-knowledge-driven mode decreases the impact of the semantic gap between iris feature data and iris physiological form on the iris recognition model, thus effectively reducing the dependence of the iris recognition model training on data. An iris recognition model aiming at the process of feature expression and recognition is tested in different iris libraries. The experiment results show that the application of data-knowledge driven strategy to iris recognition is feasible and rationality, and it can make the recognition model complete the unlimited iris category recognition which can be expanded at any time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

Data Availability

The iris data used to support the findings of this study have been deposited in the [JLU iris library] (http://www.jlucomputer.com/index/irislibrary/irislibrary.html), [CASIA iris library] (http://www.cbsr.ia.ac.cn/china/Iris%20Databases%20CH.asp). In this paper, the literature [23] and literature [24] are cited in the reference. Readers can download data by clicking on the link.

References

  1. Daugman J (2003) The importance of being random: statistical principles of iris recognition. Pattern Recogn 36(02):279–291

    Article  ADS  Google Scholar 

  2. Choudhary M, Tiwari V, Venkanna U (2020) CCRNet: a novel data-driven approach to improve cross-domain Iris recognition. Multimed Tools Appl 79(43–44):32807–32831

    Article  Google Scholar 

  3. Daugman JG (2004) How iris recognition works. IEEE Trans Circuits Syst Video Technol 14(1):21–30

    Article  Google Scholar 

  4. Guang H, Dawei L, Meng Y (2022) Iris segmentation method based on improved UNet plus. Multimed Tools Appl 81(28):41249–41269

    Article  Google Scholar 

  5. Yu J, Zhang L, Wang Z (2021) Iris Localization Algorithm based on Effective Area. Int J Antennas Propag 2021:2049646

    Article  Google Scholar 

  6. Zhuang Y, Chuah JH, Chow CO et al. (2020) Iris Recognition Using Convolutional Neural Network. presented at 2020 IEEE 10th International Conference on System Engineering And Technology, ICSET, Shah Alam, Malaysia,134–138, Nov 9, IEEE

  7. Walia S, Kumar K, Kumar M et al (2021) Fusion of Handcrafted and Deep Features for Forgery Detection in Digital Images. IEEE Access 9:99742–99755

    Article  Google Scholar 

  8. Wang CY, Muhammad J, Wang YL et al (2020) Towards Complete and Accurate Iris Segmentation Using Deep Multi-Task Attention Network for Non-Cooperative Iris Recognition. IEEE Trans Inf Forensics Secur 15:2944–2959

    Article  Google Scholar 

  9. Meenakshi K, Maragatham G (2023) An Optimised Defensive Technique to Recognize Adversarial Iris Images Using Curvelet Transform. Intell Autom Soft Comput 35(1):627–643

    Article  Google Scholar 

  10. Xu XY, Zhao B et al (2017) Research on Semantic Gap Problem of Virtual Machine. Wireless Pers Commun 97(4):5983–6004

    Article  Google Scholar 

  11. Elangovan P, Nath MK (2022) En-ConvNet: A novel approach for glaucoma detection from color fundus images using ensemble of deep convolutional neural networks. Int J Imaging Syst Technol 32(6):2034–2048

    Article  Google Scholar 

  12. Shuai L, Yuanning L, Xiaodong Z et al (2022) The two-stage recognition method based on texture signals of the heterogeneous unsteady iris. Int J Pattern Recognit Artif Intell 36(3):2250009

    Article  Google Scholar 

  13. Shuai L, Yuanning L, Xiaodong Z et al (2020) Constrained unsteady-state iris fast certification for lightweight training samples based on the scale change stable feature and multi-algorithm voting. PLoS ONE 15(5):e0232319

    Article  Google Scholar 

  14. Chen Y, Gan HM, Chen HL et al (2023) Accurate iris segmentation and recognition using an end-to-end unified framework based on MADNet and DSANet. Neurocomputing 517:264–278

    Article  Google Scholar 

  15. Ayoobi H, Kasaei H, Cao M et al (2022) Local-HDP: Interactive open-ended 3D object category recognition in real-time robotic scenarios. Robot Auton Syst 147:103911

    Article  Google Scholar 

  16. Song C, Zhang X, Zhang G (2022) Nonlinear Identification for 4-DOF Ship Maneuvering Modeling via Full-Scale Trial Data. IEEE Trans Industr Electron 69(2):1829–1835

    Article  Google Scholar 

  17. Guo H, Hu S, Wang X et al (2022) Robust Attentive Deep Neural Network for Detecting GAN-Generated Faces. IEEE Access 10:32574–32583

    Article  Google Scholar 

  18. Sukor ASA, Zakaria A et al (2019) A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes. J Intell Fuzzy Syst 36(5):4177–4188

    Article  Google Scholar 

  19. Huang JL, Xu R et al (2019) A Novel Hybrid Clustering Algorithm Based on Minimum Spanning Tree of Natural Core Points. IEEE Access 7:43707–43720

    Article  Google Scholar 

  20. Elangovan P, Nath MK (2021) Glaucoma assessment from color fundus images using convolutional neural network. Int J Imaging Syst Technol 31(2):955–971

    Article  Google Scholar 

  21. Liang W, Tang MD et al (2018) SIRSE: A secure identity recognition scheme based on electroencephalogram data with multi-factor feature. Comput Electr Eng 65:310–321

    Article  Google Scholar 

  22. Suto J, Oniga S et al (2020) Comparison of offline and real-time human activity recognition results using machine learning techniques. Neural Comput Appl 32(20):15673–15686

    Article  Google Scholar 

  23. Koul S, Kumar M, Khurana SS et al (2022) An efficient approach for copy-move image forgery detection using convolution neural network. Multimed Tools Appl 81(8):11259–11277

    Article  Google Scholar 

  24. Ding Huijun P, Zhanpeng CQ et al (2020) Multi-scale fully convolutional network for gland segmentation using three-class classification. Neurocomputing 380:150–161

    Article  Google Scholar 

  25. JLU Iris Image Database (2018) http://www.jlucomputer.com/index/irislibrary/irislibrary.html

  26. CASIA Iris Image Database (2017) http://www.cbsr.ia.ac.cn/china/Iris%20Databases%20CH.asp

  27. Wang C, Wang Y, Liu Y et al (2020) ScleraSegNet: an Attention Assisted U-Net Model for Accurate Sclera Segmentation. IEEE Trans Biom Behav Identity Sci (T-BIOM) 2(1):40–54

    Article  Google Scholar 

  28. Liu S, Liu YN, Zhu XD et al (2022) An iris quality evaluation method with pre-recognition screening function. Multimed Tools Appl 81(1):907–925

    Article  Google Scholar 

  29. Lan GW, Shen YZ, Chen TW et al (2018) Parallel implementations of structural similarity based no- reference image quality assessment. Adv Eng Softw 114:372–379

    Article  Google Scholar 

  30. Mahmood Z, Ali T, Khan SU (2016) Effects of pose and image resolution on automatic face recognition. IET Biometrics 5(2):111–119

    Article  Google Scholar 

  31. Leng Lu, Zhang J (2023) PalmHash Code vs. PalmPhasor Code. Neurocomputing 108:1–12

    Article  Google Scholar 

  32. Leng L, Zhang J, Chen G et al. (2011) Two-Directional Two-Dimensional Random Projection and Its Variations for Face and Palmprint Recognition. presented at Computational Science and Its Applications - ICCSA 2011, pp 458–470, Santander, Spain, June 20–23

  33. Karn P, He XH, Zhang J et al (2020) An experimental study of relative total variation and probabilistic collaborative representation for iris recognition. Multimed Tools Appl 79(43–44):31783–31801

    Article  Google Scholar 

  34. Ming L, Zhiqian Z, Penghui S et al (2020) Fuzzified Image Enhancement for Deep Learning in Iris Recognition. IEEE Trans Fuzzy Syst 28(1):92–99

    Article  Google Scholar 

  35. Karanwal S, Diwakar M (2021) Improved ELBP descriptors for face recognition. Int J Comput Sci Eng 25(2):198–210

    Google Scholar 

  36. Yang ZY, Zhang TH, Lu JC et al (2017) Optimizing area under the ROC curve via extreme learning machines. Knowl-Based Syst 130:74–89

    Article  Google Scholar 

  37. Yibin Y, Chengxin W, Nian P et al (2018) Noisy Image Blind Deblurring via Hyper Laplacian Prior and Spectral Properties of Convolution Kernel. Chin J Eng Math 35(6):648–654

    MathSciNet  Google Scholar 

  38. Jayanthi J, Lydia EL, Krishnaraj N et al (2021) An effective deep learning features based integrated framework for iris detection and recognition. J Ambient Intell Humaniz Comput 12(3):3271–3281

    Article  Google Scholar 

  39. Zhao TM, Liu Y, Guang H et al (2019) A Deep Learning Iris Recognition Method Based on Capsule Network Architecture. IEEE Access 7:49691–49701

    Article  Google Scholar 

  40. Dong Z, Jianbing F, Zhixin He et al (2021) An OCaNet Model Based on Octave Convolution and Attention Mechanism for Iris Recognition. Math Probl Eng 2021:3412060

    Google Scholar 

  41. Lin CD, Li XL, Li ZJ et al (2022) Finding Stars From Fireworks: Improving Non-Cooperative Iris Tracking. IEEE Trans Circuits Syst Video Technol 32(9):6137–6147

    Article  MathSciNet  Google Scholar 

Download references

Funding

This research was funded by the National Natural Science Foundation of China (NSFC), grant number 61471181; Natural Science Foundation of Jilin Province, grant number YDZJ202101ZYTS144. Thanks to the Jilin Provincial Key Laboratory of Biometrics New Technology for supporting this project.

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Shuai Liu, Yuanning Liu, Xiaodong Zhu & Shaoqiang Zhang

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Shuai Liu, Yuanning Liu, Xiaodong Zhu & Shaoqiang Zhang

Authors
  1. Shuai Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuanning Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaodong Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shaoqiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaodong Zhu.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, S., Liu, Y., Zhu, X. et al. Data-knowledge driven: a new learning strategy for iris recognition. Multimed Tools Appl 83, 27995–28025 (2024). https://doi.org/10.1007/s11042-023-16567-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16567-4

Keywords

Search

Navigation