Human face recognition system in video stream

Abstract

In the work, an analysis of detection methods and faces in the video stream and their effectiveness in real time was carried out. Modern algorithms and pre-trained models have been found to be able to recognize faces with high accuracy, but their significant drawback is, in particular, vulnerability to attacks using fake faces. Therefore, the work also analyzed approaches to detecting living faces and the possibility of their implementation in the system. Using an object-oriented approach, a tool for face capture, receiving a video stream from various sources, detecting unknown and previously captured faces in a video stream, and recognizing live faces was designed and developed. The system has been adapted to work in real time using the GPU. The work improved the architecture of a convolutional neural network for recognizing living faces with the creation of a dataset from a combination of own footage and open datasets. Also, a user interface for the face recognition system was developed. The work improved identification procedures and simplified detection of persons on video for employees of the security department of enterprises by implementing liveness detection face recognition methods. As a result of the research, a system was designed, which is intended for detection, recognition and detection of living faces in a video stream. After analyzing the known successful software products, niches that need a new solution were identified. Based on them, functional and non-functional requirements were developed. The process of recognizing faces in the video stream has been modified by implementing our own Liveness Detection model.

Prombles in programming 2024; 2-3: 296-304