With the rapid development of technology and the widespread use of video surveillance, modeling the architecture of neural networks for human recognition in video is attracting increasing attention from researchers. This article presents a study of the use of neural networks (NN) as an interdisciplinary model for classifying objects in video, including solving the problem of face search. This highlights the versatility of neural networks in integrating trained data and accurately classifying objects, which is critical for ensuring security and efficiency of video surveillance. The study uses an analysis of various neural network architectures, as well as a study of their operating algorithms. Data obtained from a literature review and experimental results allow us to evaluate the effectiveness of solving the task of classifying objects in video using various architectures, without tying the study to a specific data set. The study confirms the possibility of using modern neural network architectures for human recognition in real-time video based on the experience of experts in the field of computer vision and machine learning. The active use of neural networks as a tool for video surveillance increases the safety of infrastructure facilities and the efficiency of security services. Ultimately, this article presents an analysis of neural network architectures for facial recognition in video streams, advocating their use as a key element in the development of modern video surveillance systems and ensuring public safety.
Keywords: neural networks, neural network architectures, video surveillance systems, real-time recognition, improving security, social well-being