Iris Image Retrieval Based on Macro-features

Most iris recognition systems use the global and local texture information of the iris in order to recognize individuals. In this work, we investigate the use of macro-features that are visible on the anterior surface of RGB images of the iris for matching and retrieval. These macro-features correspond to structures such as moles, freckles, nevi, melanoma, etc. and may not be present in all iris images. Given an image of a macro-feature, the goal is to determine if it can be used to successfully retrieve the associated iris from the database. To address this problem, we use features extracted by the Scale-Invariant Feature Transform (SIFT) to represent and match macro-features. Experiments using a subset of 770 distinct irides from the Miles Research Iris Database suggest the possibility of using macro-features for iris characterization and retrieval.