Person recognition based on deep gait: a survey.

Khaliluzzaman, Md.; Uddin, Ashraf; Deb, Kaushik; Hasan, Md Junayed

Abstract

Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future.

Citation

KHALILUZZAMAN, M., UDDIN, A., DEB, K. and HASAN, M.J. 2023. Person recognition based on deep gait: a survey. Sensors [online], 23(10), article 4875. Available from: https://doi.org/10.3390/s23104875

• Journal Article Type: Article
• Acceptance Date: May 16, 2023
• Online Publication Date: May 18, 2023
• Publication Date: May 31, 2023
• Deposit Date: May 22, 2023
• Publicly Available Date: Jun 8, 2023
• Journal: Sensors
• Print ISSN: 1424-8220
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 23
• Issue: 10
• Article Number: 4875
• DOI: https://doi.org/10.3390/s23104875
• Keywords: Computer vision; Biometrics; Gait recognition; Deep learning; Gait dataset; Person recognition; Covariate; Pattern recognition
• Public URL: https://rgu-repository.worktribe.com/output/1966431