Generating Synthetic Irises by Feature Agglomeration

We propose a technique to create digital renditions of iris images that can be used to evaluate the performance of iris recognition algorithms. The proposed scheme is implemented in two stages. In the first stage, a Markov random field model is used to generate a background texture representing the global iris appearance. In the next stage a variety of iris features, viz., radial and concentric furrows, collarette and crypts, are generated and embedded in the texture field. The iris images synthesized in this manner are observed to bear close resemblance to real irises. Experiments confirm the potential of this scheme to generate a database of synthetic irises that can be used to evaluate iris recognition algorithms.