Topham, L, Khan, W, Al-Jumeily, D and Hussain, A (2022) Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models. ACM Computing Surveys. ISSN 0360-0300

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Abstract

Person identification is a problem that has received substantial attention, particularly in security domains. Gait recognition is one of the most convenient approaches enabling person identification at a distance without the need of high-quality images. There are several review studies addressing person identification such as the utilization of facial images, silhouette images, and wearable sensor. Despite skeletonbased person identification gaining popularity while overcoming the challenges of traditional approaches, existing survey studies lack the comprehensive review of skeleton-based approaches to gait identification. We present a detailed review of the human pose estimation and gait analysis that make the skeleton-based approaches possible. The study covers various types of related datasets, tools, methodologies, and evaluation metrics with associated challenges, limitations, and application domains. Detailed comparisons are presented for each of these aspects with recommendations for potential research and alternatives. A common trend throughout this paper is the positive impact that deep learning techniques are beginning to have on topics such as human pose estimation and gait identification. The survey outcomes might be useful for the related research community and other stakeholders in terms of performance analysis of existing methodologies, potential research gaps, application domains, and possible contributions in the future.

Item Type:	Article
Uncontrolled Keywords:	Human pose estimation, gait re-identification, face matching, deep learning, human pose datasets, crime suspect identification; 08 Information and Computing Sciences; Information Systems
Subjects:	H Social Sciences > HV Social pathology. Social and public welfare. Criminology Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software Q Science > QM Human anatomy
Divisions:	Computer Science & Mathematics
Publisher:	Association for Computing Machinery (ACM)
SWORD Depositor:	A Symplectic
Date Deposited:	03 May 2022 13:03
Last Modified:	15 Jun 2022 09:30
DOI or ID number:	10.1145/3533384
URI:	https://researchonline.ljmu.ac.uk/id/eprint/16740

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