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:
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
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