Abstract
We present a new approach for iris recognition based on stochastic autoregressive models with exogenous input (ARX). Iris recognition is a method to identify persons, based on the analysis of the eye iris. A typical iris recognition system is composed of four phases: image acquisition and preprocessing, iris localization and extraction, iris features characterization, and comparison and matching. The main contribution in this work is given in the step of characterization of iris features by using ARX models. In our work every iris in database is represented by an ARX model learned from data. In the comparison and matching step, data taken from iris sample are substituted into every ARX model and residuals are generated. A decision of accept or reject is taken based on residuals and on a threshold calculated experimentally. We conduct experiments with two different databases. Under certain conditions, we found a rate of successful identifications in the order of 99.7 % for one database and 100 % for the other.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
W. Boles and B. Boashash, “Iris Recognition for Biometric Identification using dyadic wavelet transform zero-crossing”, IEEE Transactions on Signal Processing, Vol. 46, No. 4, 1998, pp. 1185–1188.
J. Daugman, “How Iris Recognition Works”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 14, No. 1, 2004, pp. 21–30.
M. Dobes, L. Machala, P. Tichasvky, and J. Pospisil, “Human Eye Iris Recognition Using The Mutual Information”, Optik, No. 9, 2004, pp. 399–404.
A. Efros, and T. Leung, “Texture Synthesis by Non-Parametric Sampling”, in Proceedings of the 7th IEEE International Conference on Computer Vision, September 1999, Vol. 2, pp. 1033–1038.
G. Franklin, J. Powell, and M. Workman, Digital Control of Dynamic Systems, Addison-Wesley, 3a edition, 1997
J. Hammersley, “Monte Carlo Methods for Solving Multivariate Problems”, Annals of New York Academy of Science, 1960, No. 86, pp. 844–874.
J. Huang, Y. Wang, T. Tan, and J. Cui, “A New Iris Segmentation Method for Iris Recognition System”, In Proceedings of the 17th International Conference on Pattern Recognition, 2004, pp. 554–557.
A. Jain, A. Ross, A. Prabhakar, “An Introduction to Biometric Recognition”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 14, No. 1, 2004, pp. 4–20.
L. Liang, C. Liu, Y. Xu, B. Guo, and H. Shum, “Real-time Texture Synthesis by Patch-based Sampling”, A CM Transactions on Graphics, Vol. 20, No. 3, July 2001, pp. 127–150.
L. Ma, Y. Wang, T. Tan, and D. Zhang, “Personal Identification Based on Iris Texture Analysis”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 12, 2003, pp. 1519–1533.
D. de Martin-Roche, C. Sanchez-Avila, and R. Sanchez-Reillo, “Iris Recognition for Biometric Identification using dyadic wavelet transform zero-crossing”, In Proceedings of the IEEE 35th International Conference on Security Technology, 2001, pp. 272–277.
Miles Research. Sample iris Pictures. http://www.milesresearch.com/
M. Negin, Chmielewski T., Salganicoff M., Camus T., Cahn U., Venetianer P., and Zhang G. “An Iris Biometric System for Public and Personal Use”, Computer, Vol. 33, No. 2, 2000, pp. 70–75.
H. Proenca, and L. Alexandre, “UBIRIS: A Noisy Iris Image Database”, in Proceedings of the International Conference on Image Analysis and Processing 2005, Vol. 1, pp. 970–977.
R. Wildes, “Iris Recognition: An Emerging Biometric Technology”, Proceedings of the IEEE, Vol. 85, No. 9, 1997, pp. 1348–1363.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 International Federation for Information Processing
About this paper
Cite this paper
Garza Castañón, L.E., de Oca, S.M., Morales-Menéndez, R. (2006). An Application of ARX Stochastic Models to Iris Recognition. In: Debenham, J. (eds) Professional Practice in Artificial Intelligence. IFIP WCC TC12 2006. IFIP International Federation for Information Processing, vol 218. Springer, Boston, MA . https://doi.org/10.1007/978-0-387-34749-3_36
Download citation
DOI: https://doi.org/10.1007/978-0-387-34749-3_36
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-34655-7
Online ISBN: 978-0-387-34749-3
eBook Packages: Computer ScienceComputer Science (R0)