SpringerLink
Log in

Empirical Analysis on the Effect of Image Compression and Denoising Using Different Wavelets on Iris Recognition

  • Conference paper
  • First Online:
Advances in Computational Intelligence, Security and Internet of Things (ICCISIoT 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1192))

  • 746 Accesses

Abstract

The Iris recognition is commonly used as a security system due to its robustness against imposters. Iris datasets are huge and hence those datasets occupy more space. Iris image compression has become an important part of better performance like speed and data storage. The portable Iris system is in huge demand. That portable systems need to transmit the iris images through a very small bandwidth channel. To reduce the time for transferring a huge number of data over small bandwidth channel, iris file can be compressed to some extent to minimize the size. Another problem is that when an image is captured, it captures some noise that disturbs the recognition performance, so, denoising is required for noise-free images. This paper separately analyzes the impact of wavelet compression along with denoising on iris images. The compression analysis is done using Embedded Zero Tree Wavelet, the other technique used is Set Partitioning in Hierarchical Tree and the third technique used is Spatial-Orientation Tree Wavelet. Denoising is done using different wavelets Daubechies, Haar, Biorthogonal and Fejer-Korovkin. The impact of the wavelet compression and denoising techniques on recognition performance are compared with False Rejection Rate and False Acceptance Rate. The quality of a compressed image is calculated with different quality metrics. This work establishes that compression and denoising of the images minimally affect the recognition performance.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 42.79
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 53.49
Price includes VAT (Germany)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Trokielewicz, M., Czajka, A., Maciejewicz, P.: Iris recognition after death. IEEE Trans. Inf. Forensics Secur. 14(6), 1501–1514 (2019)

    Article  Google Scholar 

  2. Gupta, R., Sehgal, P.: Non-deterministic approach to allay replay attack on iris biometric. Pattern Anal. Appl. 22(2), 717–729 (2019)

    Article  MathSciNet  Google Scholar 

  3. Hamd, M.H., Ahmed, S.K.: Biometric system design for iris recognition using intelligent algorithms. Int. J. Mod. Educ. Comput. Sci. 11(3), 9 (2018)

    Article  Google Scholar 

  4. Shen, J.J., Yeh, C.H., Jan, J.K.: A new approach of lossy image compression based on hybrid image resizing techniques. Int. Arab J. Inf. Technol. 16(2), 226–235 (2019)

    Google Scholar 

  5. Strela, V., Heller, P.N., Strang, G., Topiwala, P., Heil, C.: The application of multiwavelet filterbanks to image processing. IEEE Trans. Image Process. 8(4), 548–563 (1999)

    Article  Google Scholar 

  6. Paul, A., Khan, T.Z., Podder, P., Ahmed, R., Rahman, M.M., Khan, M.H.: Iris image compression using wavelet transform coding. In: 2nd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 544–548. IEEE (2015)

    Google Scholar 

  7. Rakshit, S., Monro, D.M.: An evaluation of image sampling and compression for human iris recognition. IEEE Trans. Inf. Forensics Secur. 2(3), 605–612 (2017)

    Article  Google Scholar 

  8. Zemliachenko, A., Kozhemiakin, R., Vozel, B., Lukin, V.: Prediction of compression ratio in lossy compression of noisy images. In: 13th International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET), pp. 693–697. IEEE (2016)

    Google Scholar 

  9. Goyal, B., Dogra, A., Agrawal, S., Sohi, B.S.: Two-dimensional gray scale image denoising via morphological operations in NSST domain & bitonic filtering. Future Gener. Comput. Syst. 82, 158–175 (2018)

    Article  Google Scholar 

  10. Funk, W., Arnold, M., Busch, C., Munde, A.: Evaluation of image compression algorithms for fingerprint and face recognition systems. In: Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop, pp. 72–78. IEEE (2005)

    Google Scholar 

  11. Hedaoo, P., Godbole, S.S.: Wavelet thresholding approach for image denoising. Int. J. Netw. Secur. Appl. (IJNSA) 3(4), 16–21 (2011)

    Google Scholar 

  12. Dehkordi, A.B., Abu-Bakar, S.A.: Noise reduction in iris recognition using multiple thresholding. In: 2013 IEEE International Conference on Signal and Image Processing Applications, pp. 140–144. IEEE (2013)

    Google Scholar 

  13. Rodriguez, N., Barba, L.: Fejer-Korovkin wavelet based MIMO model for multi-step-ahead forecasting of monthly fishes catches. Polibits 56, 71–76 (2017)

    Google Scholar 

  14. Daugman, J., Downing, C.: Effect of severe image compression on iris recognition performance. IEEE Trans. Inf. Forensics Secur. 3(1), 52–61 (2008)

    Article  Google Scholar 

  15. Shapiro, J.M.: Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans. Signal Process. 41(12), 3445–3462 (1993)

    Article  Google Scholar 

  16. Ives, R.W., Bishop, D.A., Du, Y., Belcher, C.: Iris recognition: the consequences of image compression. EURASIP J. Adv. Signal Process. 2010(1), 680845 (2010)

    Article  Google Scholar 

  17. Ives, R.W., Broussard, R.P., Kennell, L.R., Soldan, D.L.: Effects of image compression on iris recognition system performance. J. Electron. Imaging 17(1), 011015 (2008)

    Article  Google Scholar 

  18. Varanis, M., Pederiva, R.: The influence of the wavelet filter in the parameters extraction for signal classification: an experimental study. Proc. Ser. Braz. Soc. Comput. Appl. Math. 5(1) (2017)

    Google Scholar 

  19. Ives, R.W., Bishop, D.A., Du, Y., Belcher, C.: Effects of image compression on iris recognition performance and image quality. In: 2009 IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications, pp. 16–21. IEEE (2009)

    Google Scholar 

  20. Mishra, K.N.: An efficient technique for online iris image compression and personal identification. In: Tiwari, B., Tiwari, V., Das, K.C., Mishra, D.K., Bansal, J.C. (eds.) Proceedings of International Conference on Recent Advancement on Computer and Communication. LNNS, vol. 34, pp. 335–343. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-8198-9_35

    Chapter  Google Scholar 

  21. Rai, H.M., Chatterjee, K.: Hybrid adaptive algorithm based on wavelet transform and independent component analysis for denoising of MRI images. Measurement 144, 72–82 (2019)

    Article  Google Scholar 

  22. Dua, M., Gupta, R., Khari, M., Crespo, R.G.: Biometric iris recognition using radial basis function neural network. Soft Comput. 23(22), 11801–11815 (2019)

    Article  Google Scholar 

Download references

Acknowledgement

The authors would like to acknowledge the Department of Computer Science and Engineering and TEQIP-III cell, National Institute of Technology Silchar for financial and infrastructural support to complete this research work.

Author information

Authors and Affiliations

  1. National Institute of Technology Silchar, Silchar, Assam, India

    Pranita Baro & Malaya Dutta Borah

  2. Indian Institute of Technology (ISM) Dhanbad, Dhanbad, Jharkhand, India

    Sushanta Mukhopadhyay

Authors
  1. Pranita Baro
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Malaya Dutta Borah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sushanta Mukhopadhyay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Pranita Baro or Malaya Dutta Borah .

Editor information

Editors and Affiliations

  1. National Institute of Technology Agartala, Agartala, India

    Ashim Saha

  2. National Institute of Technology Agartala, Agartala, India

    Nirmalya Kar

  3. National Institute of Technology Agartala, Agartala, India

    Suman Deb

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Baro, P., Borah, M.D., Mukhopadhyay, S. (2020). Empirical Analysis on the Effect of Image Compression and Denoising Using Different Wavelets on Iris Recognition. In: Saha, A., Kar, N., Deb, S. (eds) Advances in Computational Intelligence, Security and Internet of Things. ICCISIoT 2019. Communications in Computer and Information Science, vol 1192. Springer, Singapore. https://doi.org/10.1007/978-981-15-3666-3_30

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-3666-3_30

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3665-6

  • Online ISBN: 978-981-15-3666-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation