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Biometric-oriented Iris Identification Based on Mathematical Morphology

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Abstract

A new method for biometric identification of human irises is proposed in this paper. The method is based on morphological image processing for the identification of unique skeletons of iris structures, which are then used for feature extraction. In this approach, local iris features are represented by the most stable nodes, branches and end-points extracted from the identified skeletons. Assessment of the proposed method was done using subsets of images from the University of Bath Iris Image Database (1000 images) and the CASIA Iris Image Database (500 images). Compelling experimental results demonstrate the viability of using the proposed morphological approach for iris recognition when compared to a state-of-the-art algorithm that uses a global feature extraction approach.

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References

  1. Boles, W., & Boashash, B. (1998). A human identification technique using images of the iris and wavelet transform. IEEE Transactions on Signal Processing, 46(4), 1185–1188.

    Article  Google Scholar 

  2. Bovik, A. (Ed.) (2000). Handbook of image and video processing. Communications, networking and multimedia. San Diego: Academic Press.

    Google Scholar 

  3. Chinese Academy of Sciences’ Institute of Automation (2007). CASIA iris image database V3. http://www.cbsr.ia.ac.cn/IrisDatabase.htm.

  4. Conti, V., Milici, G., Sorbello, F., Vitabile, S. (2007). A novel iris recognition system based on micro-features. In Proceedings of the IEEE workshop on automatic identification advanced technologies (pp. 253–258). Alghero.

  5. Daugman, J. (1993). High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11), 1148–1161.

    Article  Google Scholar 

  6. Daugman, J. (1995). High confidence recognition of persons by video analysis of iris texture. In Proceedings of the European convention on security and detection (pp. 244–251). Brighton.

  7. Daugman, J. (2001). Statistical richness of visual phase information: update on recognizing persons by iris patterns. International Journal of Computer Vision, 45(1), 25–38.

    Article  MATH  Google Scholar 

  8. Gonzalez, R., & Woods, R. (1992). Digital image processing. Reading: Addison-Wesley.

  9. Grabowski, K., Sankowski, W., Napieralska, M., Zubert, M., Napieralski, A. (2006). Iris recognition algorithm optimized for hardware implementation. In Proceedings of the IEEE symposium on computational intelligence and bioinformatics and computational biology (pp. 1–5). Toronto.

  10. Kennell, L.R., Ives, R.W., Gaunt, R.M. (2006). Binary morphology and local statistics applied to iris segmentation for recognition. In Proceedings of the 13th international conference on image processing (pp. 293–296). Atlanta: IEEE Press.

    Google Scholar 

  11. Lim, S., Lee, K., Byeon, O., Kim, T. (2001). Efficient iris recognition through improvement of feature vector and classifier. ETRI Journal, 23(2), 61–70.

    Article  Google Scholar 

  12. Ma, L., Wang, Y., Tan, T. (2002). Iris recognition based on multichannel gabor filtering. In Proceedings of the 5th Asian conference on computer vision (pp. 279–283). Melbourne.

  13. Ma, L., Tan, T., Wang, Y., Zhang, D. (2004). Efficient iris recognition by characterizing key local variations. IEEE Transactions on Image Processing, 13(6), 739–750.

    Article  Google Scholar 

  14. Mansfield, A., & Wayman, J. (2002). Best practices in testing and reporting performance of biometric devices. Tech. rep., Centre for Mathematics and Scientific Computing, National Physical Laboratory, Teddington.

  15. Martin-Roche, D., Sanchez-Avila, C., Sanchez-Reillo, R. (2001). Iris recognition for biometric identification using dyadic wavelet transform zero-crossing. In Proceedings of the 35th IEEE international Carnahan conference on security technology (pp. 272–277). London.

  16. Mira Jr., J., & Mayer, J. (2003). Image feature extraction for application of biometric identification of iris: A morphological approach. In Proceedings of the 16th Brazilian symposium on computer graphics and image processing. São Carlos.

  17. Monro, D., Rakshit, S., Zhang, D. (2007). DCT-based iris recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4), 586–595.

    Article  Google Scholar 

  18. Popescu-Bodorin, N., & Balas, V.E. (2010). Comparing Haar-Hilbert and Log-Gabor based iris encoders on Bath Iris Image Database. In Proceedings of the 4th international workshop on soft computing applications (pp. 191–196). Arad: IEEE Press.

  19. Proença, H., & Alexandre, L. (2007). Toward noncooperative iris recognition: a classification approach using multiple signatures. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(4), 607–612.

    Article  Google Scholar 

  20. Schreiner, K. (1999). Biometrics: prospects for going the distance. IEEE Intelligent Systems and their Applications, 14(6), 2–6.

    Google Scholar 

  21. Serra, J. (1984). Image analysis and mathematical morphology (Vol. 1). London: Academic.

    Google Scholar 

  22. Soille, P. (2003). Morphological image analysis: Principles and applications, 2nd edn. New York: Springer.

    Google Scholar 

  23. Stiller, C., & Konrad, J. (1999). Estimating motion in image sequences. IEEE Signal Processing Magazine, 16(4), 70–91.

    Article  Google Scholar 

  24. University of Bath, Smart Sensors Ltd. (2012). Bath iris image database. http://www.smartsensors.co.uk/information/bath-iris-image-database/.

  25. Vincent, L. (1992). Morphological area opening and closing for grayscale images. In: Proceedings of the NATO shape in picture workshop (pp. 197–208). Driebergen: Springer.

    Google Scholar 

  26. Vincent, L. (1993). Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Transactions on Image Processing, 2(2), 176–201.

    Article  Google Scholar 

  27. Vincent, L. (1997). Current topics in applied morphological image analysis. In W. Kendall, O. Barndorff-Nielsen, M. van Lieshout(Eds.), Current trends in stochastic geometry and its applications. London: Chapman & Hall.

    Google Scholar 

  28. Wildes, R. (1997). Iris recognition: an emerging biometric technology. Proceedings of the IEEE, 85(9), 1347–1363.

    Article  Google Scholar 

  29. Xu, G.z., Zhang, Z.f., Ma, Y.d. (2008). An image segmentation based method for iris feature extraction. The Journal of China Universities of Posts and Telecommunications, 15(1), 96– 101, 117.

    Article  MathSciNet  Google Scholar 

  30. Ziauddin, S., & Dailey, M.N. (2009). A robust hybrid iris localization technique. In Proceedings of the 6th international conference on electrical engineering/electronics, computer, telecommunications and information technology (pp. 1058–1061). Pattaya.

  31. Zuo, J., Schmid, N.A., Chen, X. (2007). On generation and analysis of synthetic iris images. IEEE Transactions on Information Forensics and Security, 2(1), 77–90.

    Article  Google Scholar 

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Correspondence to Hugo Vieira Neto.

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de Mira, J., Neto, H.V., Neves, E.B. et al. Biometric-oriented Iris Identification Based on Mathematical Morphology. J Sign Process Syst 80, 181–195 (2015). https://doi.org/10.1007/s11265-013-0861-0

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  • DOI: https://doi.org/10.1007/s11265-013-0861-0

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