Development of skeleton-based gait models for human movement recognition based on neural networks.

Person identification based on human action recognition is a dynamic field that combines computer vision, deep learning, and motion analysis to identify and authenticate individuals based on their actions or movements. This technology finds applications in various domains, including security, surveillance, healthcare, and human-computer interaction. In this comprehensive overview, we will delve into the key components, methods, and models used in neural networks for person identification based on human action recognition. An analysis of algorithms working on the basis of these models for personal identification is presented. Also, currently, methods of evaluating and recognizing and identifying human behavior through video images are rapidly developing. Intelligent analysis and processing of video data for personal identification is one of the promising, fastest growing research areas and is widely used in various fields of human activity. In particular, it can be used to solve problems such as video surveillance for security and access to facilities for various purposes (security systems, control systems), fire protection systems, navigation, quality and quantity control of manufactured products, and many videosurveillance. This system helps to catch criminals at airports, railway stations and seaports. [ABSTRACT FROM AUTHOR]