In this study, a framework to super resolve and recognise the long range captured iris polar images is proposed. The proposed framework consists of best frame selection algorithm, modified diamond search algorithm, Gaussian process regression (GPR) based and enhanced iterated back projection (EIBP)‐based super‐resolution approach, fuzzy entropy‐based feature selector and neural network (NN) classifier. The framework uses linear kernel co‐variance function in local patch‐based GPR and EIBP algorithms and it super resolves the iris images depending on the contents of the patches, without an external dataset. NN classifier classifies the iris images by using features extracted using discrete cosine transform domain based no‐reference image quality assessment model, Gray level co‐occurrence matrix, Hu seven moments and statistical features. The framework is tested using CASIA long range iris database by comparing and analysing the peak signal‐to‐noise ratio, structural similarity index matrix and visual information fidelity in pixel domain of proposed algorithms with Yang and Nguyen framework. The results demonstrate that the proposed framework is well suited for recognition of iris images captured at a long distance.

In this study, a framework is proposed to super‐resolve the long range captured iris polar images. In this study, modified diamond search and enhanced total variation algorithms are proposed to super‐resolve the long range captured iris polar multi‐frame images. The framework is tested on Chinese Academy of Sciences' Institute of Automation (CASIA) long range iris database by comparing and analysing the structural similarity index matrix, peak signal‐to‐noise ratio, visual information fidelity in pixel domain, and execution time of proposed algorithms with Yang and Nguyen state‐of‐the‐art algorithms. The results demonstrate that the proposed framework is well suited for super‐resolution of iris images captured at a long distance.

In this paper, Iterated Back Projection and median estimators super-resolution algorithms, are implemented to increase the resolution of low resolution (LR) iris images. These two algorithms are analysed by extracting the Gray Level Co-occurrence Matrix (GLCM) features of super resolute iris images. The GLCM features of super-resolute iris images are compared with GLCM features of original iris images. The quality of resolution of enhanced image is measured using different image quality measures. It has been seen that the GLCM features of reconstructed images using above algorithms matches with that of original iris image and also the quality of enhanced image remains same

Super-resolution technique can be used to fix the low resolution problem for recognizing the iris at a distance. Two frequency domain super-resolution algorithms, Papoulis-Gerchberg (PG) and Projection onto Convex Sets, are implemented to increase the resolution of iris images. The performance analysis of these algorithms is carried out by extracting Gray Level Co-occurrence Matrix (GLCM) features of super-resoluted iris images. The super-resoluted iris region is normalized, extracted GLCM features and compared with the GLCM features of normalized original iris region. It has been observed that the GLCM features reconstructed images using above algorithm closely matches with that of original iris image. The error between the GLCM features of original normalized and normalized super-resoluted image using Papoulis-Gerchberg is less compared to that of Projection onto Convex Sets.