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.