Structured overview of the similarity metrics commonly used in 3D Skeleton-based re-identification studies

Technological advances in multi-modal devices, such as Microsoft Azure Kinect, have significantly facilitated the development of methods for visualizing and analyzing segmentally structured 3D models of the human body and movement patterns. These innovations have directly contributed to the application of this technology in 3D skeleton-based re-identification (SRID) of persons of interest. The re-identification of a person (re-ID) is a computer vision task that focuses on the recognition of complete or segmented templates and the identification of individuals in different scenes. A very important aspect of SRID research is the analytical evaluation of identity recognition using similarity metrics. These metrics enable accurate, efficient and robust re-identification of individuals based on uniquely constructed identity profiles. The aim of this article is to provide a structured overview of SRID research related to similarity metrics and their various applications.