Robust iris segmentation algorithm based on self-adaptive Chan–Vese level set model

Abstract

Iris segmentation is the first and critical step in an iris recognition system. A robust iris segmentation algorithm based on the self-adaptive Chan and Vese (SACV) level set model is proposed. First, the process of constructing the SACV model based on analyses of the corresponding requirements for the CV model is described when it is applied on iris segmentation. Second, the coarse segmentation of pupil and iris, which are localized based on image pixel gray information, is used as the initial contour of the SACV model. Third, the interference factors, such as eyelashes and eyelids, are detected and evaluated simultaneously to generate the interference degree and then the related parameter of SACV is set according to the interference degree. Finally, SACV is used to conduct the final fine segmentation of the pupil and iris. Experiments on four public iris image databases (e.g., CASIA-V1, CASIA-V3 Interval, CASIA-V3 Lamp, and MMU-V1) demonstrate the segmentation accuracy performance of the proposed algorithm, and at the same time, the proposed algorithm also displays robust performance in noisy situations, such as Gaussian, Poisson, salt-and-pepper, and speckle noises. Moreover, comparisons with the well-known methods further show that our algorithm can segment iris images more accurately.