An Indexing Method for Color Iris Images

In this work, we study the possibility of indexing color iris images. In the proposed approach, a clustering scheme on a training set of iris images is used to determine cluster centroids that capture the variations in chromaticity of the iris texture. An input iris image is indexed by comparing its pixels against these centroids and determining the dominant clusters - i.e., those clusters to which the majority of its pixels are assigned to. The cluster indices serve as an index code for the input iris image and are used during the search process, when an input probe has to be compared with a gallery of irides. Experiments using multiple color spaces convey the efficacy of the scheme on good quality images, with hit rates closes to 100% being achieved at low penetration rates.