Open Access

ARTICLE

Human Gait Recognition: A Deep Learning and Best Feature Selection Framework

Asif Mehmood¹, Muhammad Attique Khan², Usman Tariq³, Chang-Won Jeong⁴, Yunyoung Nam^5,*, Reham R. Mostafa⁶, Amira ElZeiny⁷

1 Department of Computer Science, COMSATS University Islamabad, Wah Campus, 47080, Pakistan
2 Department of Computer Science, HITEC University Taxila, Taxila, 47040, Pakistan
3 College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Khraj, Saudi Arabia
4 Medical Convergence Research Center, Wonkwang University, Iksan, Korea
5 Department of Computer Science and Engineering, Soonchunhyang University, Asan, Korea
6 Department of Information Systems, Faculty of Computers and Information Sciences, Mansoura University, Mansoura, 35516, Egypt
7 Department of Information Systems, Faculty of Computers and Information, Damietta University, Damietta, Egypt

* Corresponding Author: Yunyoung Nam. Email:

(This article belongs to the Special Issue: Recent Advances in Deep Learning, Information Fusion, and Features Selection for Video Surveillance Application)

Computers, Materials & Continua 2022, 70(1), 343-360. https://doi.org/10.32604/cmc.2022.019250

Received 07 April 2021; Accepted 10 May 2021; Issue published 07 September 2021

Abstract

Background—Human Gait Recognition (HGR) is an approach based on biometric and is being widely used for surveillance. HGR is adopted by researchers for the past several decades. Several factors are there that affect the system performance such as the walking variation due to clothes, a person carrying some luggage, variations in the view angle. Proposed—In this work, a new method is introduced to overcome different problems of HGR. A hybrid method is proposed or efficient HGR using deep learning and selection of best features. Four major steps are involved in this work-preprocessing of the video frames, manipulation of the pre-trained CNN model VGG-16 for the computation of the features, removing redundant features extracted from the CNN model, and classification. In the reduction of irrelevant features Principal Score and Kurtosis based approach is proposed named PSbK. After that, the features of PSbK are fused in one materix. Finally, this fused vector is fed to the One against All Multi Support Vector Machine (OAMSVM) classifier for the final results. Results—The system is evaluated by utilizing the CASIA B database and six angles 00°, 18°, 36°, 54°, 72°, and 90° are used and attained the accuracy of 95.80%, 96.0%, 95.90%, 96.20%, 95.60%, and 95.50%, respectively. Conclusion—The comparison with recent methods show the proposed method work better.

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Citations