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Published in:
Design of High-Speed LVDS Data Communication Link Using FPGA Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Face Super Resolution by Tangential and Exponential Kernel Weighted Regression Model

Authors : B. Deshmukh Amar, N. Usha Rani

Published in: Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2

Publisher: Springer International Publishing

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Abstract

The need of recognizing individual from the low resolution non-frontal picture is hard hassle in video surveillance. In an effort to alleviate the hassle of popularity in low decision photograph, literature presents unique strategies for face recognition after converting the low decision photograph to excessive resolution. For this reason, this paper provides a method for multi-view face video notable decision using the tangential and exponential kernel weighted regression model. In this paper, a brand new hybrid kernel is proposed to carry out non-parametric kernel regression version for estimation of neighbor pixel within the first-rate decision after the face detection is done the usage of Viola-Jones algorithms. The experimentation is finished with the U.S. Face video databases and the quantitative results are analyzed the usage of the SDME with the prevailing strategies. From the result final results, we prove that the most SDME of 77.3 db is obtained for the proposed approach compared with the existing techniques like, nearest interpolation, bicubic interpolation and bilinear interpolation.

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Metadata
Title
Face Super Resolution by Tangential and Exponential Kernel Weighted Regression Model
Authors
B. Deshmukh Amar
N. Usha Rani
Copyright Year
2018
Book
Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 2

Print ISBN: 978-3-319-63644-3

Electronic ISBN: 978-3-319-63645-0

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-63645-0_2

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