Abstract
Iris-based biometric recognition outperforms other biometric methods in terms of accuracy. In this paper an iris normalization model for iris recognition is proposed, which combines linear and non-linear methods to unwrap the iris region. First, non-linearly transform all iris patterns to a reference annular zone with a predefined λ, which is the ratio of the radii of inner and outer boundaries of the iris. Then linearly unwrap this reference annular zone to a fix-sized rectangle block for subsequence processing. Our iris normalization model is illuminated by the ‘minimum-wear-and-tear’ meshwork of the iris and it is simplified for iris recognition. This model explicitly shows the non-linear property of iris deformation when pupil size changes. And experiments show that it does better than the over-simplified linear normalization model and will improve the iris recognition performance.
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© 2005 Springer-Verlag Berlin Heidelberg
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Yuan, X., Shi, P. (2005). A Non-linear Normalization Model for Iris Recognition. In: Li, S.Z., Sun, Z., Tan, T., Pankanti, S., Chollet, G., Zhang, D. (eds) Advances in Biometric Person Authentication. IWBRS 2005. Lecture Notes in Computer Science, vol 3781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569947_17
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DOI: https://doi.org/10.1007/11569947_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29431-3
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