Abstract
The location of the texture limits in an iris image is a previous step in the person’s recognition processes. The iris localization plays a very important role because the speed and performance of an iris recognition system is limited by the results of iris localization to a great extent. It includes finding the iris boundaries (inner and outer). We present a new method for iris pupil contours delimitation and its practical application to iris texture features estimation and isolation. Two different strategies for estimating the inner and outer iris contours are used. The results obtained in the determination of internal contour is used efficiently in the search of the external contour parameters employing a differential integral operator. The proposed algorithm takes advantage of the pupil’s circular form using well-known elements of analytic geometry, in particular, the determination of the bounded circumference to a triangle. The algorithm validation experiments were developed in images taken with near infrared illumination, without the presence of specular light in their interior. Satisfactory time results were obtained (minimum 0.0310 s, middle 0.0866 s, maximum 0.1410 s) with 98% of accuracy. We will continue working in the algorithm modification for using with images taken under not controlled conditions.
Chapter PDF
Similar content being viewed by others
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.
References
Díaz, Y., Gil, J.L.: Algoritmo para la detección del contorno de la pupila del ojo humano. I Conferencia Científica de la Universidad de las Ciencias Informáticas, UCIENCIA 2005. ISBN 959-16-0318-5 (2005)
Daugman, J.: High Confidence Visual Recognition of Persons by a Test of Statistical Independence. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11), 1148–1160 (1993)
Daugman, J.: Demodulation by Complex-Valued Wavelets for Stochastic Pattern Recognition. International Journal of Wavelets, Multiresolution and Information Processing 1(1), 1–17 (2003)
Center for Biometrics and Security Research (CBSR). CASIA Iris Image Database, http://www.sinobiometrics.com
Cui, J., Wang, Y., Tan, T., Ma, L., Sun, Z.: A Fast and Robust Iris Localization Method Based on Texture Segmentation, Center for Biometric Authentication and Testing, National Laboratory of Pattern Recognition. Disponible en internet (2005)
Wildes, R.: Iris Recognition: An Emerging Biometric Technology. Proceedings of the IEEE 85, 1348–1363 (1997)
Camus, T.A., Wildes, R.: Reliable and Fast Eye Finding in Close-up Images. In: Proceedings of the IEEE International Conference on Pattern Recognition (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rodríguez, J.L.G., Rubio, Y.D. (2005). A New Method for Iris Pupil Contour Delimitation and Its Application in Iris Texture Parameter Estimation. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_66
Download citation
DOI: https://doi.org/10.1007/11578079_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
eBook Packages: Computer ScienceComputer Science (R0)