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Iris Recognition Using Integer Wavelet Transform and Log Energy Entropy

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Advances in Computing and Network Communications

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 736))

Abstract

As the technology is reaching its next level day by day, the concerns over information or data security are also creeping up. Biometric systems have been widely used in many real-world applications in order to provide more security to the data. Iris recognition system has become a widely used system for human identification from the last few decades. In this paper, an efficient iris recognition system is proposed where iris localization is carried out by first finding the pupil-iris boundary using the connected component analysis approach. And then by considering the pupil center as the reference point, it traverses through the virtual outer boundary to detect the iris-sclera boundary. After applying normalization on the iris region, the iris region is partitioned into non-overlapping blocks. Further, a combination of integer wavelet transform (IWT) with log energy entropy (LEE) is applied on each block to extract the unique iris code as the feature vector. The experiments have been conducted using the multimodal biometric database, SDUMLA-HMT. The proposed system has succeeded in achieving a low false acceptance rate and a very low false rejection rate. Also, the uniqueness of the iris patterns is evaluated in terms of degrees of freedom and is found to be a promising one.

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References

  1. Adamović, S., Miškovic, V., Maček, N., Milosavljević, M., Šarac, M., Saračević, M., Gnjatović, M.: An efficient novel approach for iris recognition based on stylometric features and machine learning techniques. Future Gener. Comput. Syst. 107, 144–157 (2020)

    Article  Google Scholar 

  2. Bhateja, A.K., Sharma, S., Chaudhury, S., Agrawal, N.: Iris recognition based on sparse representation and k-nearest subspace with genetic algorithm. Pattern Recogn. Lett. 73, 13–18 (2016)

    Article  Google Scholar 

  3. Calderbank, A.R., Daubechies, I., Sweldens, W., Yeo, B.L.: Wavelet transforms that map integers to integers. Appl. Comput. Harmonic Anal. 5(3), 332–369 (1998)

    Article  MathSciNet  Google Scholar 

  4. Chen, J.S., Huertas, A., Medioni, G.: Fast convolution with Laplacian-of-Gaussian masks. IEEE Trans. Pattern Anal. Mach. Intell. 4, 584–590 (1987)

    Article  Google Scholar 

  5. Chen, C.H., Chu, C.T.: High performance iris recognition based on 1-D circular feature extraction and PSO-PNN classifier. Expert Syst. Appl. 36(7), 10351–10356 (2009)

    Article  Google Scholar 

  6. Chen, J., Shen, F., Chen, D.Z., Flynn, P.J.: Iris recognition based on human-interpretable features. IEEE Trans. Inf. Forensics Secur. 11(7), 1476–1485 (2016)

    Article  Google Scholar 

  7. Das, A.B., Bhuiyan, M.I.H.: Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain. Biomed. Sig. Process. Control 29, 11–21 (2016)

    Article  Google Scholar 

  8. Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148–1161 (1993)

    Article  Google Scholar 

  9. 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 

  10. Ekici, S., Yildirim, S., Poyraz, M.: Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition. Expert Syst. Appl. 34(4), 2937–2944 (2008)

    Article  Google Scholar 

  11. Fernandez, J., Annapoorani, G., Krishnamoorthy, R.: A simple boundary extraction technique for irregular pupil localization. Int. J. Inf. Technol. Comput. Sci. Perspect. 2(1), 370–376 (2013)

    Google Scholar 

  12. Göksu, H.: Ground moving target recognition using log energy entropy of wavelet packets. Electron. Lett. 54(4), 233–235 (2018)

    Article  Google Scholar 

  13. Göksu, H.: BCI oriented EEG analysis using log energy entropy of wavelet packets. Biomed. Sig. Process. Control 44, 101–109 (2018)

    Article  Google Scholar 

  14. Jincy, J.F., Nithyanandam, P., Chavali, R., Appalaghe, A.K.: Image theft identification using biometric feature. Special issue: Advances in Smart Computing and Bioinformatics, Asian J. Pharm. Clin.Res., pp. 385–389 (2017)

    Google Scholar 

  15. Fernandez, J.J., Pandian, N.: Fingerprint core point detection using Connected component approach and Orientation map edge tracing approach. Int. J. Biometrics (in press)

    Google Scholar 

  16. Johny, A., Fernandez, J.J.: Breast cancer detection in mammogram using fuzzy C-means and random forest classifier. Int. J. Sci. Res. Sci. Eng. Technol. 4(8), 312–321 (2018)

    Google Scholar 

  17. Kapadia, A.M., Pandian, N.: Reversible data hiding methods in integer wavelet transform. Int. J. Inf. Comput. Secur. 12(1), 70–89 (2020)

    Google Scholar 

  18. Karthiga, R., Narasimhan, K.: Automated diagnosis of breast cancer using wavelet based entropy features. In: Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 274–279 (2018)

    Google Scholar 

  19. Kaudki, O., Bhurchandi, K.: A robust iris recognition approach using fuzzy edge processing technique. In: 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, pp. 1–6 (2018)

    Google Scholar 

  20. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  21. Kumar, M., Pachori, R.B., Acharya, U.R.: Automated diagnosis of atrial fibrillation ECG signals using entropy features extracted from flexible analytic wavelet transform. Biocybern. Biomed. Eng. 38(3), 564–573 (2018)

    Article  Google Scholar 

  22. Minaee, S., Abdolrashidi, A., Wang, Y.: Iris recognition using scattering transform and textural features. In: IEEE Signal Processing and Signal Processing Education Workshop (SP/SPE), pp. 37–42 (2015)

    Google Scholar 

  23. Nalla, P.R., Kumar, A.: Toward more accurate iris recognition using cross-spectral matching. IEEE Trans. Image Process. 26(1), 208–221 (2016)

    Article  MathSciNet  Google Scholar 

  24. Patil, P., Vasanth, K.: Iris recognition using local and global iris image moment features. In: Innovations in Power and Advanced Computing Technologies (i-PACT), vol. 1, p. 1–5 (2019)

    Google Scholar 

  25. Pǎvǎloi, L., Niţǎ, C. D.: Iris recognition using SIFT descriptors with different distance measures. In: 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. 1–4 (2018)

    Google Scholar 

  26. Pedersen, S.J.K.: Circular hough transform. Vision, graphics, and interactive systems. Aalborg University 123(6) (2007)

    Google Scholar 

  27. Proenca, H.: Iris recognition: on the segmentation of degraded images acquired in the visible wavelength. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1502–1516 (2009)

    Article  Google Scholar 

  28. Specht, D.F.: Probabilistic neural networks. Neural Netw. 3(1), 109–118 (1990)

    Article  Google Scholar 

  29. Trokielewicz, M., Czajka, A., Maciejewicz, P.: Post-mortem iris recognition with deep-learning-based image segmentation. Image Vis. Comput. 94, 103866 (2020)

    Article  Google Scholar 

  30. Umer, S., Dhara, B.C., Chanda, B.: Iris recognition using multiscale morphologic features. Pattern Recogn. Lett. 65, 67–74 (2015)

    Article  Google Scholar 

  31. Yin, Y., Liu, L., Sun, X.: SDUMLA-HMT: a multimodal biometric database. In: Chinese Conference on Biometric Recognition, pp. 260–268 (2011)

    Google Scholar 

  32. Tisse, C.L., Martin, L., Torres, L., Robert, M.: Person identification technique using human iris recognition. In: Proceedings of Vision Interface, pp. 294–299 (2002)

    Google Scholar 

  33. Wildes, R.P.: Iris recognition: an emerging biometric technology. Proc. IEEE 85(9), 1348–1363 (1997)

    Article  Google Scholar 

  34. Zhang, J. S., Chen, C. J.: Local variance projection log energy entropy features for illumination robust face recognition. In: International Symposium on Biometrics and Security Technologies, pp. 1–5 (2008)

    Google Scholar 

  35. Zhao, T., Liu, Y., Huo, G., Zhu, X.: A deep learning iris recognition method based on capsule network architecture. IEEE Access 7, 49691–49701 (2019)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Research Scholar, VIT University, Chennai, India

    Jincy J. Fernandez

  2. Professor, VIT University, Chennai, India

    Nithyanandam Pandian

Authors
  1. Jincy J. Fernandez
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  2. Nithyanandam Pandian
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Corresponding author

Correspondence to Jincy J. Fernandez .

Editor information

Editors and Affiliations

  1. School of CSE, Indian Institute of Information Technology and Management-Kerala, Trivandrum, Kerala, India

    Sabu M. Thampi

  2. Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland

    Erol Gelenbe

  3. School of Computer Science, University of Oklahoma, Norman, OK, USA

    Mohammed Atiquzzaman

  4. Department of Computer Science, University at Buffalo, State University, Buffalo, NY, USA

    Vipin Chaudhary

  5. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

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Fernandez, J.J., Pandian, N. (2021). Iris Recognition Using Integer Wavelet Transform and Log Energy Entropy. In: Thampi, S.M., Gelenbe, E., Atiquzzaman, M., Chaudhary, V., Li, KC. (eds) Advances in Computing and Network Communications. Lecture Notes in Electrical Engineering, vol 736. Springer, Singapore. https://doi.org/10.1007/978-981-33-6987-0_2

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  • DOI: https://doi.org/10.1007/978-981-33-6987-0_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-6986-3

  • Online ISBN: 978-981-33-6987-0

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