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

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


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.


Iris recognition Quality metric Image compression Image denoising 



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.


  1. 1.
    Trokielewicz, M., Czajka, A., Maciejewicz, P.: Iris recognition after death. IEEE Trans. Inf. Forensics Secur. 14(6), 1501–1514 (2019)CrossRefGoogle Scholar
  2. 2.
    Gupta, R., Sehgal, P.: Non-deterministic approach to allay replay attack on iris biometric. Pattern Anal. Appl. 22(2), 717–729 (2019)MathSciNetCrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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. 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)CrossRefGoogle Scholar
  6. 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. 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)CrossRefGoogle Scholar
  8. 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. 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)CrossRefGoogle Scholar
  10. 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. 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. 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. 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. 14.
    Daugman, J., Downing, C.: Effect of severe image compression on iris recognition performance. IEEE Trans. Inf. Forensics Secur. 3(1), 52–61 (2008)CrossRefGoogle Scholar
  15. 15.
    Shapiro, J.M.: Embedded image coding using zerotrees of wavelet coefficients. IEEE Trans. Signal Process. 41(12), 3445–3462 (1993)CrossRefGoogle Scholar
  16. 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)CrossRefGoogle Scholar
  17. 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)CrossRefGoogle Scholar
  18. 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. 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. 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). Scholar
  21. 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)CrossRefGoogle Scholar
  22. 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)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.National Institute of Technology SilcharSilcharIndia
  2. 2.Indian Institute of Technology (ISM) DhanbadDhanbadIndia

Personalised recommendations