A Non-linear Normalization Model for Iris Recognition

摘要

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.