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Super-resolution generative adversarial network based on the dual dimension attention mechanism for biometric image super-resolution
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Huang, Chi-En, Li, Yung-Hui, Aslam, Muhammad Saqlain and Chang, Ching-Chun (2021) Super-resolution generative adversarial network based on the dual dimension attention mechanism for biometric image super-resolution. Sensors, 21 (23). e7817. doi:10.3390/s21237817 ISSN 1424-8220.
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Official URL: https://doi.org/10.3390/s21237817
Abstract
There exist many types of intelligent security sensors in the environment of the Internet of Things (IoT) and cloud computing. Among them, the sensor for biometrics is one of the most important types. Biometric sensors capture the physiological or behavioral features of a person, which can be further processed with cloud computing to verify or identify the user. However, a low-resolution (LR) biometrics image causes the loss of feature details and reduces the recognition rate hugely. Moreover, the lack of resolution negatively affects the performance of image-based biometric technology. From a practical perspective, most of the IoT devices suffer from hardware constraints and the low-cost equipment may not be able to meet various requirements, particularly for image resolution, because it asks for additional storage to store high-resolution (HR) images, and a high bandwidth to transmit the HR image. Therefore, how to achieve high accuracy for the biometric system without using expensive and high-cost image sensors is an interesting and valuable issue in the field of intelligent security sensors. In this paper, we proposed DDA-SRGAN, which is a generative adversarial network (GAN)-based super-resolution (SR) framework using the dual-dimension attention mechanism. The proposed model can be trained to discover the regions of interest (ROI) automatically in the LR images without any given prior knowledge. The experiments were performed on the CASIA-Thousand-v4 and the CelebA datasets. The experimental results show that the proposed method is able to learn the details of features in crucial regions and achieve better performance in most cases.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
SWORD Depositor: | Library Publications Router | ||||||
Library of Congress Subject Headings (LCSH): | Biometric identification, High resolution imaging, Neural networks (Computer science), Deep learning (Machine learning) | ||||||
Journal or Publication Title: | Sensors | ||||||
Publisher: | MDPI | ||||||
ISSN: | 1424-8220 | ||||||
Official Date: | 24 November 2021 | ||||||
Dates: |
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Volume: | 21 | ||||||
Number: | 23 | ||||||
Article Number: | e7817 | ||||||
DOI: | 10.3390/s21237817 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 6 December 2021 | ||||||
Date of first compliant Open Access: | 7 December 2021 | ||||||
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