Abstract
In this work, Gaussian Process Regression (GPR) based novel framework is proposed to super resolute the long range captured iris polar images. The framework uses linear kernel co-variance function in GPR during the process of super resolution of iris image, without external dataset. The new technique is proposed to reduce the time taken to super resolute the iris polar image patches. The framework is tested using benchmark images as well as CASIA long range iris database by comparing and analyzing the peak signal to noise ratio (PSNR) and structural similarity index matrix (SSIM) of proposed algorithms with the existing algorithms. Empirical results indicate that the proposed framework, which improves PSNR up to 36 dB and promotes structural similarity index measurement (SSIM) up to 0.92 in averages, is better than the other existing method. The results demonstrate that the proposed approach outperforms some of the state-of-the-art super resolution approaches.
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