IRIS super-resolution via nonparametric over-complete dictionary learning

This paper presents a novel iris super-resolution approach using a powerful nonparametric Bayesian modeling technique in the framework of sparse representation and over-complete dictionary. Far apart from previous iris super-resolution methods, our proposed approach has ability to automatically discover optimal parameter sets and optimally adapt from a given training data. Particularly, the Beta Process will be employed to build a nonparametric discriminative over-complete dictionary to represent and discriminate input samples simultaneously. Our proposed method will be evaluated on Casia iris database and compared with the linear interpolation super resolution. The result shows that our approach improves the performance of iris recognition.