Abstract
Current iris recognition systems can achieve high level of success under restricted conditions, while they still face challenges of utilizing images with heavy deformation caused by illumination variations. Developing methods to alleviate the deformation becomes a necessity, since the requirement of uniform lighting is often not practical. This paper introduces a novel algorithm to counteract elastic iris deformation. In the proposed algorithm, for nonlinear iris stretch, the distance of any point in the iris region to the pupil boundary is assumed to be the corresponding distance under linear stretch plus an additive deviation. Gaussian function is employed to model the deviation. Experimental results on two databases with nonlinear deformation demonstrate the effectiveness of the algorithm. The proposed iris deformation correction algorithm achieves a lower Equal Error Rate (EER), compared to the other two linear and nonlinear normalization methods in the literature, making the system more robust in realistic environments.
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
Daugman, J.: High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11), 1148–1161 (1993)
Yuan, X., Shi, P.: A non-linear normalization model for iris recognition. In: Advances in Biometric Person Authentication, pp. 135–141 (2005)
Wyatt, H.J.: A ’minimum-wear-and-tear’ meshwork for the iris. Vision Research 40, 2167–2176 (2000)
Li, X.: Modeling intra-class variation for nonideal iris recognition. In: International Conference on Biometrics 2006, pp. 419–427 (2006)
Rohen: Der bau der regenbogenhaut beim menschen undeinigen saugern. Gegenbaur Morphology Journal 91, 140–181 (1951)
Ma, L., Tan, T., Wang, Y., Zhang, D.: Personal identification based on iris texture analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(12), 1519–1533 (2003)
CASIA-IrisV3: http://www.cbsr.ia.ac.cn/IrisDatabase.htm
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wei, Z., Tan, T., Sun, Z. (2007). Nonlinear Iris Deformation Correction Based on Gaussian Model. In: Lee, SW., Li, S.Z. (eds) Advances in Biometrics. ICB 2007. Lecture Notes in Computer Science, vol 4642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74549-5_82
Download citation
DOI: https://doi.org/10.1007/978-3-540-74549-5_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74548-8
Online ISBN: 978-3-540-74549-5
eBook Packages: Computer ScienceComputer Science (R0)