Deep Learning Based Gait Recognition Using Convolutional Neural Network in the COVID-19 Pandemic
Gait recognition is the behavioral biometric trait that tracks humans based on their walking motion. It has gained attention because of its non-invasive and unobtrusive behaviors and applicable to the different application area. In this paper, we target model-free gait recognition with the deep lear...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Ital Publication
2022-08-01
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| Series: | Emerging Science Journal |
| Subjects: | |
| Online Access: | https://www.ijournalse.org/index.php/ESJ/article/view/980 |
| _version_ | 1811321232204759040 |
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| author | Md Shohel Shohel Sayeed Pa Pa Min Md Ahsanul Bari |
| author_facet | Md Shohel Shohel Sayeed Pa Pa Min Md Ahsanul Bari |
| author_sort | Md Shohel Shohel Sayeed |
| collection | DOAJ |
| description | Gait recognition is the behavioral biometric trait that tracks humans based on their walking motion. It has gained attention because of its non-invasive and unobtrusive behaviors and applicable to the different application area. In this paper, we target model-free gait recognition with the deep learning approach for the Muslim community in the COVID-19 pandemic. The different convolutional neural network architectures (CNN) are examined by using the spatio-temporal gait representation called Gait Energy Images (GEI). We explored both the identification and verification problems to determine the suitability of the proposed CNN frameworks. In gait recognition, the intraclass variation is larger than the inter-class variation because of the shooting view, the walking speed, the wearing condition, and so on. To tackle this challenge, the verification framework is more suitable for the 1:1 association of gait recognition. As for the verification problem, we implemented the Siamese network with the parallel CNN architecture. All the proposed methods are tested against the public gait datasets called OUISIR-LP and OUISIR-MVLP to determine the identification and verification performance in terms of recognition accuracy and error rate.
Doi: 10.28991/ESJ-2022-06-05-012
Full Text: PDF |
| first_indexed | 2024-04-13T13:13:14Z |
| format | Article |
| id | doaj.art-b4cf772084624b23a61e052ecf57038b |
| institution | Directory Open Access Journal |
| issn | 2610-9182 |
| language | English |
| last_indexed | 2024-04-13T13:13:14Z |
| publishDate | 2022-08-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | Emerging Science Journal |
| spelling | doaj.art-b4cf772084624b23a61e052ecf57038b2022-12-22T02:45:33ZengItal PublicationEmerging Science Journal2610-91822022-08-01651086109910.28991/ESJ-2022-06-05-012363Deep Learning Based Gait Recognition Using Convolutional Neural Network in the COVID-19 PandemicMd Shohel Shohel Sayeed0Pa Pa Min1Md Ahsanul Bari2Faculty of Information Science and Technology, Multimedia University, 75450 Melaka,Faculty of Information Science and Technology, Multimedia University, 75450 Melaka,Faculty of Information Science and Technology, Multimedia University, 75450 Melaka,Gait recognition is the behavioral biometric trait that tracks humans based on their walking motion. It has gained attention because of its non-invasive and unobtrusive behaviors and applicable to the different application area. In this paper, we target model-free gait recognition with the deep learning approach for the Muslim community in the COVID-19 pandemic. The different convolutional neural network architectures (CNN) are examined by using the spatio-temporal gait representation called Gait Energy Images (GEI). We explored both the identification and verification problems to determine the suitability of the proposed CNN frameworks. In gait recognition, the intraclass variation is larger than the inter-class variation because of the shooting view, the walking speed, the wearing condition, and so on. To tackle this challenge, the verification framework is more suitable for the 1:1 association of gait recognition. As for the verification problem, we implemented the Siamese network with the parallel CNN architecture. All the proposed methods are tested against the public gait datasets called OUISIR-LP and OUISIR-MVLP to determine the identification and verification performance in terms of recognition accuracy and error rate. Doi: 10.28991/ESJ-2022-06-05-012 Full Text: PDFhttps://www.ijournalse.org/index.php/ESJ/article/view/980deep learningconvolutional neural networkgait recognitioncovid-19 pandemic. |
| spellingShingle | Md Shohel Shohel Sayeed Pa Pa Min Md Ahsanul Bari Deep Learning Based Gait Recognition Using Convolutional Neural Network in the COVID-19 Pandemic Emerging Science Journal deep learning convolutional neural network gait recognition covid-19 pandemic. |
| title | Deep Learning Based Gait Recognition Using Convolutional Neural Network in the COVID-19 Pandemic |
| title_full | Deep Learning Based Gait Recognition Using Convolutional Neural Network in the COVID-19 Pandemic |
| title_fullStr | Deep Learning Based Gait Recognition Using Convolutional Neural Network in the COVID-19 Pandemic |
| title_full_unstemmed | Deep Learning Based Gait Recognition Using Convolutional Neural Network in the COVID-19 Pandemic |
| title_short | Deep Learning Based Gait Recognition Using Convolutional Neural Network in the COVID-19 Pandemic |
| title_sort | deep learning based gait recognition using convolutional neural network in the covid 19 pandemic |
| topic | deep learning convolutional neural network gait recognition covid-19 pandemic. |
| url | https://www.ijournalse.org/index.php/ESJ/article/view/980 |
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