Abstract
The use of iris recognition for human authentication has been spreading in the past years. Daugman has proposed a method for iris recognition, composed by four stages: segmentation, normalization, feature extraction, and matching. In this paper we propose some modifications and extensions to Daugman’s method to cope with noisy images. These modifications are proposed after a study of images of CASIA and UBIRIS databases. The major modification is on the computationally demanding segmentation stage, for which we propose a faster and equally accurate template matching approach. The extensions on the algorithm address the important issue of pre-processing, that depends on the image database, being mandatory when we have a non infra-red camera, like a typical WebCam. For this scenario, we propose methods for reflection removal and pupil enhancement and isolation. The tests, carried out by our C# application on grayscale CASIA and UBIRIS images, show that the template matching segmentation method is more accurate and faster than the previous one, for noisy images. The proposed algorithms are found to be efficient and necessary when we deal with non infra-red images and non uniform illumination.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arvacheh, E.M.: A study of segmentation and normalization for iris recognition systems. Master’s thesis, University of Waterloo (2006)
Boles, W.: A security system based on iris identification using wavelet transform. In: Jain, L.C. (ed.) First International Conference on Knowledge-Based Intelligent Electronic Systems, Adelaide, Australia, pp. 533–541 (1997)
Daugman, J.: High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(11), 1148–1161 (1993)
Daugman, J.: How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 21–30 (2004)
Greco, J., Kallenborn, D., Nechyba, M.C.: Statistical pattern recognition of the iris. In: 17th annual Florida Conference on the Recent Advances in Robotics, FCRAR (2004)
Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology 14(1) (2004)
Huang, J., Wang, Y., Tan, T., Cui, J.: A new iris segmentation method for recognition. In: 17th Int. Conf. on Pattern Recognition, ICPR 2004 (2004)
Joung, B.J., Kim, J.O., Chung, C.H., Lee, K.S., Yim, W.Y., Lee, S.H.: On improvement for normalizing iris region for a ubiquitous computing. In: Gervasi, O., Gavrilova, M.L., Kumar, V., Laganá, A., Lee, H.P., Mun, Y., Taniar, D., Tan, C.J.K. (eds.) ICCSA 2005. LNCS, vol. 3480, pp. 1213–1219. Springer, Heidelberg (2005)
Lim, J.: Two-dimensional Signal and Image Processing. Prentice-Hall, Englewood Cliffs (1990)
Maltoni, D., Maio, D., Jain, A., Prabhakar, S.: Handbook of Fingerprint Recognition, 1st edn. Springer, Heidelberg (2005)
Masek, L.: Recognition of human iris patterns for biometric identification. Master’s thesis, University of Western Australia (2003)
Proença, H., Alexandre, L.: UBIRIS: a noisy iris image database. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 970–977. Springer, Heidelberg (2005)
Proença, H., Alexandre, L.: A Method for the Identification of Noisy Regions in Normalized Iris Images. In: 18th International Conference on Pattern Recognition (ICPR 2006), Hong Kong, August 20-24, vol. 4, pp. 405–408 (2006)
Proença, H.: Towards Non-Cooperative Biometric Iris Recognition. PhD thesis, Universidade da Beira Interior (2007)
Vatsa, M., Singh, R., Gupta, P.: Comparison of iris recognition algorithms. In: Proceedings of International Conference on Intelligent Sensing and Information Processing, pp. 354–358 (2004)
Wildes, R.: Iris recognition: an emerging biometric technology. Proceedings of the IEEE 85(9), 1348–1363 (1997)
Yao, P., Li, J., Ye, X., Zhuang, Z., Li, B.: Analysis and improvement of an iris identification algorithm. In: 18th International Conference on Pattern Recognition (ICPR 2006), Hong Kong, August 20-24, vol. 4, pp. 362–365 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ferreira, A., Lourenço, A., Pinto, B., Tendeiro, J. (2010). Tuning Iris Recognition for Noisy Images. In: Fred, A., Filipe, J., Gamboa, H. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2009. Communications in Computer and Information Science, vol 52. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11721-3_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-11721-3_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11720-6
Online ISBN: 978-3-642-11721-3
eBook Packages: Computer ScienceComputer Science (R0)