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Title: | Super-Low Resolution Face Recognition using Integrated Efficient Sub-Pixel Convolutional Neural Network (ESPCN) and Convolutional Neural Network (CNN) |
Authors: | Talab, Mohammed Awang, Suryanti Najim, Saif Al-din |
Keywords: | Super-Resolution (SR), Face Recognition Low Resolution (LR Deep Learning |
Issue Date: | 29-Jun-2019 |
Publisher: | 2019 IEEE International Conference on Automatic Control and Intelligent Systems |
Abstract: | 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. |
URI: | http://localhost:8080/xmlui/handle/123456789/5737 |
Appears in Collections: | قسم علوم الحاسبات |
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