Citation

Sayeed, Md. Shohel and Yusof, Ibrahim and Abdullah, Mohd Fikri Azli and Bari, Md Ahsanul and Pa, Pa Min (2023) A comprehensive survey on deep-learning based gait recognition for humans in the COVID-19 pandemic. Indonesian Journal of Electrical Engineering and Computer Science, 30 (2). p. 882. ISSN 2502-4752

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Abstract

Human gait recognition is a biometric technique that has been utilized for security purposes for the last decade. Gait recognition is an appealing biometric modality that aims to identify individuals based on the way they walk. The outbreak of the novel coronavirus (COVID-19), has spread across the world. The number of people infected with COVID-19 is rising rapidly throughout the world. Even though some vaccines for this pandemic have been developed to minimize the effects of COVID-19, deep learning-based gait recognition techniques have shown themselves to be an effective tool for identifying the individuals wearing face mask in COVID-19 pandemic. These techniques play an important part in reducing the rate of COVID-19 spreading throughout the world in the context of the COVID-19 pandemic. Deep learning methods are currently dominating the state-of-the-art in gait recognition and have fostered real-world applications. The main objective of this paper is to provide a comprehensive overview of recent advancements in gait recognition with deep learning, including datasets, test protocols, stateof-the-art solutions, challenges, and future research directions. The purpose of this discussion is to identify current challenges that need to be addressed as well as to suggest some directions for future research that could be explored

Item Type:	Article
Uncontrolled Keywords:	Convolutional neural network COVID-19 pandemic Deep learning Gait analysis Gait energy image Gait recognition Recurrent neural networks
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	07 Apr 2023 01:25
Last Modified:	07 Apr 2023 01:25
URII:	http://shdl.mmu.edu.my/id/eprint/11292

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