Super-low resolution face recognition using integrated Efficient Sub-Pixel Convolutional Neural Network (ESPCN) and Convolutional Neural Network (CNN)

Several deep image-based models which depend on deep learning have shown great success in the recorded computational and reconstruction efficiencies, especially for single high-resolution images. In the past, the use of superresolution was commonly characterized by interference, and hence, the need...

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Main Authors:	Talab, Mohammed Ahmed, Suryanti, Awang, Najim, Saif Al-din M.
Format:	Conference or Workshop Item
Language:	English English
Published:	Universiti Malaysia Pahang 2019
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/26467/ http://umpir.ump.edu.my/id/eprint/26467/ http://umpir.ump.edu.my/id/eprint/26467/1/55.%20Super-low%20resolution%20face%20recognition%20using%20integrated%20Efficient.pdf http://umpir.ump.edu.my/id/eprint/26467/2/55.1%20Super-low%20resolution%20face%20recognition%20using%20integrated%20Efficient.pdf

id	ump-26467
recordtype	eprints
spelling	ump-264672019-12-23T04:34:56Z http://umpir.ump.edu.my/id/eprint/26467/ Super-low resolution face recognition using integrated Efficient Sub-Pixel Convolutional Neural Network (ESPCN) and Convolutional Neural Network (CNN) Talab, Mohammed Ahmed Suryanti, Awang Najim, Saif Al-din M. QA76 Computer software Several deep image-based models which depend on deep learning have shown great success in the recorded computational and reconstruction efficiencies, especially for single high-resolution images. In the past, the use of superresolution was commonly characterized by interference, and hence, the need for a model with higher performance. This study proposed a method for low to super-resolution face recognition, called efficient sub-pixel convolution neural network. This is a convolutional neural network which is usually employed at the time of image pre-processing to increase the chances of recognizing images with low resolution. The proposed Efficient Sub-Pixel Convolutional Neural Network is used for the conversion of low-resolution images into a high-resolution format for onward recognition. This conversion is based on the features extracted from the image. Using several evaluation tools, the proposed Efficient Sub-Pixel Convolutional Neural Network recorded a higher performance in terms of image resolution when compared to the performance of the benchmarked traditional methods. The evaluations were carried out on a Yale face database and ORL dataset faces. For Yale and ORL datasets, the obtained accuracy of the proposed method was 95.3% and 93.5%, respectively, which were higher than those of the other related methods. Universiti Malaysia Pahang 2019 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/26467/1/55.%20Super-low%20resolution%20face%20recognition%20using%20integrated%20Efficient.pdf pdf en http://umpir.ump.edu.my/id/eprint/26467/2/55.1%20Super-low%20resolution%20face%20recognition%20using%20integrated%20Efficient.pdf Talab, Mohammed Ahmed and Suryanti, Awang and Najim, Saif Al-din M. (2019) Super-low resolution face recognition using integrated Efficient Sub-Pixel Convolutional Neural Network (ESPCN) and Convolutional Neural Network (CNN). In: IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS 2019), 29 June 2019 , Shah Alam, Malaysia. pp. 1-5.. ISBN 978-1-7281-0784-4 https://doi.org/10.1109/I2CACIS.2019.8825083
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Talab, Mohammed Ahmed Suryanti, Awang Najim, Saif Al-din M. Super-low resolution face recognition using integrated Efficient Sub-Pixel Convolutional Neural Network (ESPCN) and Convolutional Neural Network (CNN)
description	Several deep image-based models which depend on deep learning have shown great success in the recorded computational and reconstruction efficiencies, especially for single high-resolution images. In the past, the use of superresolution was commonly characterized by interference, and hence, the need for a model with higher performance. This study proposed a method for low to super-resolution face recognition, called efficient sub-pixel convolution neural network. This is a convolutional neural network which is usually employed at the time of image pre-processing to increase the chances of recognizing images with low resolution. The proposed Efficient Sub-Pixel Convolutional Neural Network is used for the conversion of low-resolution images into a high-resolution format for onward recognition. This conversion is based on the features extracted from the image. Using several evaluation tools, the proposed Efficient Sub-Pixel Convolutional Neural Network recorded a higher performance in terms of image resolution when compared to the performance of the benchmarked traditional methods. The evaluations were carried out on a Yale face database and ORL dataset faces. For Yale and ORL datasets, the obtained accuracy of the proposed method was 95.3% and 93.5%, respectively, which were higher than those of the other related methods.
format	Conference or Workshop Item
author	Talab, Mohammed Ahmed Suryanti, Awang Najim, Saif Al-din M.
author_facet	Talab, Mohammed Ahmed Suryanti, Awang Najim, Saif Al-din M.
author_sort	Talab, Mohammed Ahmed
title	Super-low resolution face recognition using integrated Efficient Sub-Pixel Convolutional Neural Network (ESPCN) and Convolutional Neural Network (CNN)
title_short	Super-low resolution face recognition using integrated Efficient Sub-Pixel Convolutional Neural Network (ESPCN) and Convolutional Neural Network (CNN)
title_full	Super-low resolution face recognition using integrated Efficient Sub-Pixel Convolutional Neural Network (ESPCN) and Convolutional Neural Network (CNN)
title_fullStr	Super-low resolution face recognition using integrated Efficient Sub-Pixel Convolutional Neural Network (ESPCN) and Convolutional Neural Network (CNN)
title_full_unstemmed	Super-low resolution face recognition using integrated Efficient Sub-Pixel Convolutional Neural Network (ESPCN) and Convolutional Neural Network (CNN)
title_sort	super-low resolution face recognition using integrated efficient sub-pixel convolutional neural network (espcn) and convolutional neural network (cnn)
publisher	Universiti Malaysia Pahang
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/26467/ http://umpir.ump.edu.my/id/eprint/26467/ http://umpir.ump.edu.my/id/eprint/26467/1/55.%20Super-low%20resolution%20face%20recognition%20using%20integrated%20Efficient.pdf http://umpir.ump.edu.my/id/eprint/26467/2/55.1%20Super-low%20resolution%20face%20recognition%20using%20integrated%20Efficient.pdf
first_indexed	2023-09-18T22:41:15Z
last_indexed	2023-09-18T22:41:15Z
_version_	1777416929364934656

Super-low resolution face recognition using integrated Efficient Sub-Pixel Convolutional Neural Network (ESPCN) and Convolutional Neural Network (CNN)

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