Noisy iris image matching by using multiple cues

Noisy iris recognition under visible lighting has recently drawn much attention. This paper proposes an effective method for visible light iris image matching by using multiple characteristics of iris and eye images. The method consists of image preprocessing, iris data matching, eye data matching, and multi-modal fusion. Ordinal measures and color analysis are adopted for iris data matching, and texton representation and semantic information are used for eye data matching. After we obtain the four matching scores, a robust score level fusion strategy is applied to generate the dissimilarity measure of the two images under consideration. Extensive experiments on the UBIRIS.v2 database and the NICE.II training dataset demonstrate that the proposed method is effective. Our method significantly outperforms all other algorithms submitted to the Noisy Iris Challenge Evaluation-Part II (NICE.II), an open contest in noisy iris image matching.

► A framework is proposed for noisy iris image matching by using multiple cues.
► Iris localization is achieved by using both iris images and mask images.
► Ordinal measures and color histogram are adopted to characterize iris region.
► Texton histogram and semantic information are used for eye region representation.
► A robust score level fusion method is applied for multimodal fusion.