Synthesis of large realistic iris databases using patch-based sampling
This paper presents a framework to synthesize large realistic iris databases, providing an alternative to iris database collection. Firstly, iris patch is used as a basic element to characterize visual primitive of iris texture, and patch-based sampling is applied to create an iris prototype. Then a set of pseudo irises with intra-class variations are derived from the prototype. Qualitative and quantitative studies reveal that synthetic databases are well suited for evaluating iris recognition systems by achieving three goals: (1) the synthetic iris images bear a close resemblance to real iris images in terms of visual appearance; (2) the proposed framework is able to generate databases with large capacity; (3) statistical performance shows that the synthetic iris images hold all the major characteristics of real iris images.