Indonesian Journal of Electrical Engineering and Computer Science Vol. 30, No. 2, May 2023, pp. 882~902 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v30.i2.pp882-902  882 Journal homepage: http://ijeecs.iaescore.com A comprehensive survey on deep-learning based gait recognition for humans in the COVID-19 pandemic Md Shohel Sayeed, Ibrahim Bin Yusof, Mohd Fikri Azli bin Abdullah, Md Ahsanul Bari, Pa Pa Min Centre for Intelligent Cloud Computing, Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia Article Info ABSTRACT Article history: Received Sep 3, 2022 Revised Nov 29, 2022 Accepted Dec 26, 2022 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, state- of-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. Keywords: Convolutional neural network COVID-19 pandemic Deep learning Gait analysis Gait energy image Gait recognition Recurrent neural networks This is an open access article under the CC BY-SA license. Corresponding Author: Md Shohel Sayeed Centre for Intelligent Cloud Computing, Faculty of Information Science and Technology Multimedia University 75450 Melaka, Malaysia Email: shohel.sayeed@mmu.edu.my 1. INTRODUCTION In recent years have shown the rise of a new coronavirus known as COVID-19, which was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This virus has caused a pandemic around the world [1]. Both number of people who have been infected as well as the mortality rate is rising rapidly. Since the time when this article is being written, it has been reported that more than 108,000,000 persons have been infected with COVID-19, the number of death cases is around 2,400,000, and the number of patients who have recovered is approximately 80,000,000 [2]. The widespread transmission of COVID-19 has resulted in the quarantining of a significant section of the world's population and the destruction of a broad variety of industrial sectors, both of which have contributed to the current state of global economic instability. Fever, dry cough, myalgia, dyspnea, and headache are some of the most usual symptoms of the new coronavirus [3]. In recent decades, a subfield of biometric identification known as gait recognition has emerged. Using the intrinsic patterns of movement in individuals to detect them, this subfield focuses on detecting them based on their physical characteristics, such as their size and relationships with the trunk and the limbs, as well as space- time information about their intrinsic movement patterns [4]. Gait recognition is a subfield of biometric