The Human Gait Recognition using an Enhanced Convolutional Neural Network
Gait is a soft biometric with unique advantages compared to other biometrics. Soft biometric are features that can be extracted remotely and do not require human interaction. The force of gait, is that it does not require cooperative subjects and it is recognizable from low-resolution surveillance...
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| Format: | Article |
| Language: | English |
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College of science, university of Diyala
2024-07-01
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| Series: | Academic Science Journal |
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| Online Access: | https://acadscij.uodiyala.edu.iq/index.php/Home/article/view/180 |
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| _version_ | 1849221182126030848 |
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| author | Fatima Esmail Ziyad Tariq Mustafa Al-Ta'i |
| author_facet | Fatima Esmail Ziyad Tariq Mustafa Al-Ta'i |
| author_sort | Fatima Esmail |
| collection | DOAJ |
| description |
Gait is a soft biometric with unique advantages compared to other biometrics. Soft biometric are features that can be extracted remotely and do not require human interaction. The force of gait, is that it does not require cooperative subjects and it is recognizable from low-resolution surveillance videos. This paper presents a proposed framework for gait recognition by building the required dataset. In this work, nine gait attributes are extracted, and recognition is done using an Enhanced Convolutional Neural Network (ECNN). The proposed model achieved an accuracy of 89.583%.
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| format | Article |
| id | doaj-art-3bc13d0997444b07bfb904712200c5d9 |
| institution | Kabale University |
| issn | 2958-4612 2959-5568 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | College of science, university of Diyala |
| record_format | Article |
| series | Academic Science Journal |
| spelling | doaj-art-3bc13d0997444b07bfb904712200c5d92024-11-19T10:00:14ZengCollege of science, university of DiyalaAcademic Science Journal2958-46122959-55682024-07-012310.24237/ASJ.02.03.796BThe Human Gait Recognition using an Enhanced Convolutional Neural NetworkFatima Esmail0Ziyad Tariq Mustafa Al-Ta'iAL-Timeme Gait is a soft biometric with unique advantages compared to other biometrics. Soft biometric are features that can be extracted remotely and do not require human interaction. The force of gait, is that it does not require cooperative subjects and it is recognizable from low-resolution surveillance videos. This paper presents a proposed framework for gait recognition by building the required dataset. In this work, nine gait attributes are extracted, and recognition is done using an Enhanced Convolutional Neural Network (ECNN). The proposed model achieved an accuracy of 89.583%. https://acadscij.uodiyala.edu.iq/index.php/Home/article/view/180Gait recognitionSoft BiometricsMediaPipeEnhanced Convolutional Neural Networks |
| spellingShingle | Fatima Esmail Ziyad Tariq Mustafa Al-Ta'i The Human Gait Recognition using an Enhanced Convolutional Neural Network Academic Science Journal Gait recognition Soft Biometrics MediaPipe Enhanced Convolutional Neural Networks |
| title | The Human Gait Recognition using an Enhanced Convolutional Neural Network |
| title_full | The Human Gait Recognition using an Enhanced Convolutional Neural Network |
| title_fullStr | The Human Gait Recognition using an Enhanced Convolutional Neural Network |
| title_full_unstemmed | The Human Gait Recognition using an Enhanced Convolutional Neural Network |
| title_short | The Human Gait Recognition using an Enhanced Convolutional Neural Network |
| title_sort | human gait recognition using an enhanced convolutional neural network |
| topic | Gait recognition Soft Biometrics MediaPipe Enhanced Convolutional Neural Networks |
| url | https://acadscij.uodiyala.edu.iq/index.php/Home/article/view/180 |
| work_keys_str_mv | AT fatimaesmail thehumangaitrecognitionusinganenhancedconvolutionalneuralnetwork AT ziyadtariqmustafaaltai thehumangaitrecognitionusinganenhancedconvolutionalneuralnetwork AT fatimaesmail humangaitrecognitionusinganenhancedconvolutionalneuralnetwork AT ziyadtariqmustafaaltai humangaitrecognitionusinganenhancedconvolutionalneuralnetwork |