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Super resolution and recognition of unconstrained ear image

Super resolution and recognition of unconstrained ear image In this paper, a framework is proposed to super-resolve low resolution ear images and to recognise these images, without external dataset. This frame uses linear kernel co-variance function-based Gaussian process regression to super-resolve the ear images. The performance of the proposed framework is evaluated on UERC database by comparing and analysing the peak signal to noise ratio, structural similarity index matrix and visual information fidelity in pixel domain. The results are compared with the state-of-the-art-algorithms. The results demonstrate that the proposed approach outperforms the state-of-the-art super resolution approaches. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Biometrics Inderscience Publishers

Super resolution and recognition of unconstrained ear image

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1755-8301
eISSN
1755-831X
DOI
10.1504/IJBM.2020.110813
Publisher site
See Article on Publisher Site

Abstract

In this paper, a framework is proposed to super-resolve low resolution ear images and to recognise these images, without external dataset. This frame uses linear kernel co-variance function-based Gaussian process regression to super-resolve the ear images. The performance of the proposed framework is evaluated on UERC database by comparing and analysing the peak signal to noise ratio, structural similarity index matrix and visual information fidelity in pixel domain. The results are compared with the state-of-the-art-algorithms. The results demonstrate that the proposed approach outperforms the state-of-the-art super resolution approaches.

Journal

International Journal of BiometricsInderscience Publishers

Published: Jan 1, 2020

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