Abstract
This article examines the assessment of tools such as Dlib, OpenCV, MTCNN, FaceNet for face recognition. In the process of work, the execution time and the count of detecting of each tool were determined and calculated. The results pictured in graph choose the right tool according to the data obtained in the article that was optimal for next research works. The choice was made for the ease of writing a parallel algorithm. The rationale for the choice of the tool is also given according to the parameters of the use of machine resources, which makes it possible to optimally select a machine without additional and large costs. A comparative analysis of each instrument was performed and the results were identified accordingly. Based on the test results, we divided two cases and tried to give recommendations for each of them. The first case is triggered if only quick face detection is considered in the video. The second case is triggered if more faces are viewed in the video. It turned out that in the first case, we need to use the Dlib tool. In the second case, we can choose tools like Facenet or Mtcnn. The results obtained in the process of the research are presented in the form of graphs, tables and recorded in the conclusion section of this article.