Quality-driven super-resolution for less constrained iris recognition at a distance and on the move

Less constrained iris identification systems at a distance and on the move suffer from poor resolution and poor quality of the captured iris images, which significantly degrades iris recognition performance. This paper proposes a new signal-level fusion approach which incorporates a quality score into a reconstruction-based super-resolution process to generate a high-resolution iris image from a low-resolution and quality inconsistent video sequence of an eye. A novel approach for assessing the focus level of the iris image, which is invariant to lighting and oclusion conditions, is introduced. The focus score is combined with several other quality factors to perform the quality weighted super-resolution where the highest quality frames contribute the greatest amount of information to the resulting high-resolution images without introducing spurious high-frequency components. Experiments conducted on the Multiple Biometric Grand Challenge portal dataset show that our proposed approach outperforms the traditional best quality frame selection approach and other existing state-of-the-art signal-level and score-level fusion approaches for recognition of less constrained iris at a distance and on the move