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Article

Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion

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Department of Computer Science, HITEC University Taxila, Taxila 47080, Pakistan
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College of Computer Science and Engineering, University of Ha’il, Ha’il 55211, Saudi Arabia
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College of Computer Engineering and Science, Prince Sattam Bin Abdulaziz University, Al-Kharaj 11942, Saudi Arabia
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Department of Electrical Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi Arabia
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Department of Applied Artificial Intelligence, Ajou University, Suwon 16499, Korea
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Faculty of Applied Computing and Technology, Noroff University College, 4608 Kristiansand, Norway
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Author to whom correspondence should be addressed.
Academic Editors: Dawid Połap, Gautam Srivastava, Marta Wlodarczyk-Sielicka and Jiayi Ma
Sensors 2021, 21(22), 7584; https://doi.org/10.3390/s21227584
Received: 16 September 2021 / Revised: 8 November 2021 / Accepted: 12 November 2021 / Published: 15 November 2021
Human Gait Recognition (HGR) is a biometric technique that has been utilized for security purposes for the last decade. The performance of gait recognition can be influenced by various factors such as wearing clothes, carrying a bag, and the walking surfaces. Furthermore, identification from differing views is a significant difficulty in HGR. Many techniques have been introduced in the literature for HGR using conventional and deep learning techniques. However, the traditional methods are not suitable for large datasets. Therefore, a new framework is proposed for human gait recognition using deep learning and best feature selection. The proposed framework includes data augmentation, feature extraction, feature selection, feature fusion, and classification. In the augmentation step, three flip operations were used. In the feature extraction step, two pre-trained models were employed, Inception-ResNet-V2 and NASNet Mobile. Both models were fine-tuned and trained using transfer learning on the CASIA B gait dataset. The features of the selected deep models were optimized using a modified three-step whale optimization algorithm and the best features were chosen. The selected best features were fused using the modified mean absolute deviation extended serial fusion (MDeSF) approach. Then, the final classification was performed using several classification algorithms. The experimental process was conducted on the entire CASIA B dataset and achieved an average accuracy of 89.0. Comparison with existing techniques showed an improvement in accuracy, recall rate, and computational time. View Full-Text
Keywords: gait recognition; biometric; data augmentation; deep learning; features optimization; features fusion gait recognition; biometric; data augmentation; deep learning; features optimization; features fusion
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MDPI and ACS Style

Saleem, F.; Khan, M.A.; Alhaisoni, M.; Tariq, U.; Armghan, A.; Alenezi, F.; Choi, J.-I.; Kadry, S. Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion. Sensors 2021, 21, 7584. https://doi.org/10.3390/s21227584

AMA Style

Saleem F, Khan MA, Alhaisoni M, Tariq U, Armghan A, Alenezi F, Choi J-I, Kadry S. Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion. Sensors. 2021; 21(22):7584. https://doi.org/10.3390/s21227584

Chicago/Turabian Style

Saleem, Faizan, Muhammad Attique Khan, Majed Alhaisoni, Usman Tariq, Ammar Armghan, Fayadh Alenezi, Jung-In Choi, and Seifedine Kadry. 2021. "Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion" Sensors 21, no. 22: 7584. https://doi.org/10.3390/s21227584

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