Abstract
Iris recognition is a reliable technique for identification of people. 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. We are introducing stoch- astic autoregressive with exogenous inputs models for the features characterization step. Every model is learned from data. In the comparison and matching step, data taken from iris sample are substituted into every model and residuals are generated. A decision is taken based on a threshold calculated experimentally. A successful rate of identifications for UBIRIS and MILES databases shows potential applications.
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
Chen, C., Chu, C.: Low Complexity iris Recognition Based on Wavelet Probabilistic Neural Networks. In: Proc. of the Int. Joint Conf. on Neural Networks, vol. 3, pp. 1930–1935 (2005)
Daugman, J.: How Iris Recognition Works. IEEE Trans. on Circuits and Systems for Video Technology 14(1), 21–30 (2004)
Dobes, M., Machala, L., Tichasvky, P., Pospisil, J.: Human Eye Iris Recognition Using The Mutual Information. Optik (9), 399–404 (2004)
Franklin, G., Powell, J., Workman, M.: Digital Control of Dynamic Systems, 3rd edn. Addison-Wesley, Reading (1997)
Ganeshan, B., Theckedath, D., Young, R., Chatwin, C.: Biometric Iris Recognition System Using a Fast and Robust Iris Localization and Alignment Procedure. Optics and Lasers in Eng. 44, 1–24 (2006)
Garza Castañon, L.E., Montes de Oca, S., Morales-Menéndez, R.: An Application of Random and Hammersley Sampling Methods to Iris Recognition. In: To appear in the 19th Int. Conf. on Industrial, Engineering & Other Applications of Applied Intelligent SystemsOptics and Lasers in Eng., Annecy, Fr (June 2006)
Huang, J., Wang, Y., Tan, T., Cui, J.: A New Iris Segmentation Method for Iris Recognition System. In: Proc. of the 17th Int Conf on Pattern Recognition, pp. 554–557 (2004)
Independent Testing of Iris Recognition Technology Final Report, International Biometric Group (May 2005)
Jain, A., Ross, A., Prabhakar, A.: An Introduction to Biometric Recognition. IEEE Trans. on Circuits and Systems for Video Technology 14(1), 4–20 (2004)
Ma, L., Wang, Y., Tan, T., Zhang, D.: Personal Identification Based on Iris Texture Analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(12), 1519–1533 (2003)
Miles Research. Sample iris Pictures, http://www.milesresearch.com/
Negin, M., Chmielewski, T., Salganicoff, M., Camus, T., Cahn, U., Venetianer, P., Zhang, G.: An Iris Biometric System for Public and Personal Use. Computer 33(2), 70–75 (2000)
Proenca, H., Alexandre, L.: UBIRIS: A Noisy Iris Image Database. In: Proc of the Int. Conf. on Image Analysis and Processing, vol. 1, pp. 970–977 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Castañón, L.E.G., de Oca, S.M., Morales-Menéndez, R. (2006). Feature Characterization in Iris Recognition with Stochastic Autoregressive Models. In: Sichman, J.S., Coelho, H., Rezende, S.O. (eds) Advances in Artificial Intelligence - IBERAMIA-SBIA 2006. IBERAMIA SBIA 2006 2006. Lecture Notes in Computer Science(), vol 4140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11874850_21
Download citation
DOI: https://doi.org/10.1007/11874850_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45462-5
Online ISBN: 978-3-540-45464-9
eBook Packages: Computer ScienceComputer Science (R0)