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Erschienen in:
Design of High-Speed LVDS Data Communication Link Using FPGA Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Face Super Resolution by Tangential and Exponential Kernel Weighted Regression Model

verfasst von : B. Deshmukh Amar, N. Usha Rani

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

Verlag: 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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Vorheriges Kapitel Design of High-Speed LVDS Data Communication Link Using FPGA
Nächstes Kapitel Monitoring of Distributed Resources Based on Client-Server (single-hop) Mobile Agents in Homogeneous Network of Interconnected Nodes
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Metadaten
Titel
Face Super Resolution by Tangential and Exponential Kernel Weighted Regression Model
verfasst von
B. Deshmukh Amar
N. Usha Rani
Copyright-Jahr
2018
Buch
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-Jahr: 2018

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

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