Open Access

ARTICLE

Human Gait Recognition Based on Sequential Deep Learning and Best Features Selection

Ch Avais Hanif¹, Muhammad Ali Mughal^1,*, Muhammad Attique Khan², Usman Tariq³, Ye Jin Kim⁴, Jae-Hyuk Cha⁴

1 Department of Electrical Engineering, HITEC University, Taxila, Pakistan
2 Department of Computer Science, HITEC University, Taxila, Pakistan
3 College of Computer Engineering and Science, Prince Sattam Bin Abdulaziz University, Al-Kharaj, 11942, Saudi Arabia
4 Department of Computer Science, Hanyang University, Seoul, 04763, Korea

* Corresponding Author: Muhammad Ali Mughal. Email:

Computers, Materials & Continua 2023, 75(3), 5123-5140. https://doi.org/10.32604/cmc.2023.038120

Received 28 November 2022; Accepted 22 February 2023; Issue published 29 April 2023

Abstract

Gait recognition is an active research area that uses a walking theme to identify the subject correctly. Human Gait Recognition (HGR) is performed without any cooperation from the individual. However, in practice, it remains a challenging task under diverse walking sequences due to the covariant factors such as normal walking and walking with wearing a coat. Researchers, over the years, have worked on successfully identifying subjects using different techniques, but there is still room for improvement in accuracy due to these covariant factors. This paper proposes an automated model-free framework for human gait recognition in this article. There are a few critical steps in the proposed method. Firstly, optical flow-based motion region estimation and dynamic coordinates-based cropping are performed. The second step involves training a fine-tuned pre-trained MobileNetV2 model on both original and optical flow cropped frames; the training has been conducted using static hyperparameters. The third step proposed a fusion technique known as normal distribution serially fusion. In the fourth step, a better optimization algorithm is applied to select the best features, which are then classified using a Bi-Layered neural network. Three publicly available datasets, CASIA A, CASIA B, and CASIA C, were used in the experimental process and obtained average accuracies of 99.6%, 91.6%, and 95.02%, respectively. The proposed framework has achieved improved accuracy compared to the other methods.

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Keywords

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