Human Body Pose Estimation for Gait Identification: A Comprehensive
  Survey of Datasets and Models
Luke K. Topham Wasiq Khan Dhiya Al-Jumeily Abir Hussain
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 skeleton-based 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.