Feature-domain super-resolution for iris recognition

• We propose a framework for iris super-resolution (SR) using Gabor-based nonlinear features.
• In the literature, there exists only SR using biometric linear features, which are not the most discriminant features, especially for iris.
• This feature-domain SR framework incorporates iris model information in form of prior probability to constrain the reconstruction.
• Extensive experiments have confirmed the validity of the framework.
• This SR framework can also be applicable to any other biometrics modalities.

Uncooperative iris identification systems at a distance suffer from poor resolution of the acquired iris images, which significantly degrades iris recognition performance. Super-resolution techniques have been employed to enhance the resolution of iris images and improve the recognition performance. However, most existing super-resolution approaches proposed for the iris biometric super-resolve pixel intensity values, rather than the actual features used for recognition. This paper thoroughly investigates transferring super-resolution of iris images from the intensity domain to the feature domain. By directly super-resolving only the features essential for recognition, and by incorporating domain specific information from iris models, improved recognition performance compared to pixel domain super-resolution can be achieved. A framework for applying super-resolution to nonlinear features in the feature-domain is proposed. Based on this framework, a novel feature-domain super-resolution approach for the iris biometric employing 2D Gabor phase-quadrant features is proposed. The approach is shown to outperform its pixel domain counterpart, as well as other feature domain super-resolution approaches and fusion techniques.