Skip to main content
SpringerLink
Log in

End to End Robust Recognition Method for Iris Using a Dense Deep Convolutional Neural Network

  • Conference paper
  • First Online:
Biometric Recognition (CCBR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11818))

Included in the following conference series:

Abstract

Many algorithms of iris recognition have been proposed in academic field. Due to the iris image is obscured by illumination, blur and occlusion, iris recognition has not been widely adapted in life, the robustness of iris recognition algorithm is required to be higher. Hence, this paper proposes an end to end dense deep convolutional neural network (DDNet) for the iris recognition. DDNet used a deeper network structure and used the segmented images as input images without prior preprocessing or other conventional image processing techniques. The performance of the DDNet is tested on CASIA-Iris-V3 and IITD, from 138 and 224 different subjects respectively. Experiment results showed that DDNet is adapted and robust in different parameters, and its performance over most existing algorithms.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Sun, Z., Tan, T.: Ordinal measures for iris recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2211–2226 (2008)

    Google Scholar 

  2. Abhyankar, A., Schuckers, S.: Iris quality assessment and bi-orthogonal wavelet based encoding for recognition. Pattern Recogn. 42(9), 1878–1894 (2009)

    Article  Google Scholar 

  3. Minaee, S., Abdolrashidi, A.A, Wang, Y.: Iris recognition using scattering transform and textural features. In: IEEE Signal Process. and Signal Process. Educ. Workshop, pp. 37–42. IEEE (2015)

    Google Scholar 

  4. Al-Waisy, A.S., Qahwaji, R., et al.: A multi-biometric iris recognition system based on a deep learning approach. Pattern Anal. Appl. 21(3), 783–802 (2018)

    Article  MathSciNet  Google Scholar 

  5. Gangwar, A., Joshi, A.: DeepIrisNet: deep iris representation with applications in iris recognition and cross-sensor iris recognition. In: Proceedings of the IEEE International Conference on Image Processing, pp. 2301–2305. IEEE (2016)

    Google Scholar 

  6. Dat, N., Tuyen, P., Young, L., et al.: Deep learning-based enhanced presentation attack detection for iris recognition by combining features from local and global regions based on NIR camera sensor. Sensors 18(8), 2601 (2018)

    Article  Google Scholar 

  7. Chinese Academy of Science—Institute of Automation, CASIAIRISV3 Iris Image Database Version 3.0. http://biometrics.idealtest.org/dbDetailForUser.do?id=3

  8. Kumar, A., Passi, A.: Comparison and combination of iris matchers for reliable personal authentication. Pattern Recogn. 43(3), 1016–1026 (2010)

    Article  Google Scholar 

  9. LabelMe [EB/OL], 16 January 2016. https://github.com/wkentaro/LabelMe

  10. Hofbauer, H., Alonso-Fernandez, F., Wild, P., et al.: A ground truth for iris segmentation. In: International Conference on Pattern Recognition, pp. 527-532. IEEE (2014)

    Google Scholar 

  11. Ma, L., Wang, Y., Tan, T.: Iris recognition using circular symmetric filters. In: International Conference on Pattern Recognition. IEEE (2002)

    Google Scholar 

  12. Costa, R.M.D., Gonzaga, A.: Dynamic features for iris recognition. IEEE Trans. Syst. Man Cybern. B Cybern. 42(4), 1072–1082 (2012)

    Article  Google Scholar 

  13. Vatsa, M., Singh, R., Noore, A.: Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing. IEEE Trans. 38(4), 1021–1035 (2008)

    Google Scholar 

  14. Kerim, A.A., Mohammed, S.J.: New iris feature extraction and pattern matching based on statistical measurement. Int J Emerg Trends Technol Comput Sci. 3(5), 226–231 (2014)

    Google Scholar 

  15. Nabti, M., Bouridane, A.: New active contours approach and phase wavelet maxima to improve iris recognition system. In: European Workshop on Visual Information Processing, pp. 238–244. IEEE (2013)

    Google Scholar 

  16. Umer, S., Dhara, B.C., Chanda, B.: Texture code matrix-based multi-instance iris recognition. Pattern Anal. Appl. 19(1), 283–295 (2016)

    Article  MathSciNet  Google Scholar 

  17. Bharath, B.V., Vilas, A.S., Manikantan, K., et al.: Iris recognition using radon transform thresholding-based feature extraction with Gradient-based Isolation as a pre-processing technique. In: International Conference on Industrial and Information Systems, pp. 1–8. IEEE (2015)

    Google Scholar 

  18. Elgamal, M., Al-Biqami, N.: An efficient feature extraction method for iris recognition based on wavelet transformation. Int. J. Comput. Inf. Technol. 2(03), 521–526 (2013)

    Google Scholar 

  19. Dhage, S.S., Hegde, S.S., Manikantan, K., et al.: DWT-based feature extraction and radon transform based contrast enhancement for improved iris recognition. Procedia Comput. Sci. 45, 256–265 (2015)

    Article  Google Scholar 

  20. Chen, Y., Wang, W., Zeng, Z., et al.: An adaptive CNNs technology for robust iris segmentation. IEEE Access 7, 64517–64532 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Nanchang Hangkong University, Nanchang, 330063, JiangXi, China

    Ying Chen, Zhuang Zeng & Fei Hu

Authors
  1. Ying Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhuang Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fei Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhuang Zeng .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  2. Chinese Academy of Sciences, Beijing, China

    Ran He

  3. Tsinghua University, Beijing, China

    Jianjiang Feng

  4. Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

  5. Tsinghua University, Shenzhen, China

    Zhenhua Guo

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chen, Y., Zeng, Z., Hu, F. (2019). End to End Robust Recognition Method for Iris Using a Dense Deep Convolutional Neural Network. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-31456-9_41

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31455-2

  • Online ISBN: 978-3-030-31456-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation