Toward accurate localization and high recognition performance for noisy iris images

Iris recognition plays an important role in biometrics. Until now, many scholars have made different efforts in this field. However, the recognition performances of most proposed methods degrade dramatically when the image contains some noise, which inevitably occurs during image acquisition such as reflection spots, inconsistent illumination, eyelid, eyelash, hair, etc. In this paper, an accurate iris localization and high recognition performance approach for noisy iris images is presented. After filling the reflection spots using the inpainting method which is based on Navier-Stokes (NS) equations, the Probable boundary (Pb) edge detection operator is used to detect pupil edge initially, which can eliminate the interference of inconsistent illumination, eyelid, eyelash and hair. Besides, the accurate circle parameters are obtained in delicately to reduce the input space of Hough transforms. The iris feature code is constructed based on 1D Log-Gabor filter. Our thorough experimental results on the challenging iris image database CASIA-Iris-Thousand achieve an EER of 1.8272 %, which outperforms the state-of-the-art methods.