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...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Universiti Malaysia Pahang
2019
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| Subjects: | |
| Online Access: | 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 |
| _version_ | 1848822543911223296 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T02:42:55Z |
| format | Conference or Workshop Item |
| id | ump-26467 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T02:42:55Z |
| publishDate | 2019 |
| publisher | Universiti Malaysia Pahang |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-264672024-01-08T04:50:43Z 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 (Published) https://doi.org/10.1109/I2CACIS.2019.8825083 |
| 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) |
| title | 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_short | 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) |
| topic | QA76 Computer software |
| 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 |