The Study of Mathematical Models and Algorithms for Face Recognition in Images Using Python in Proctoring System
Ardak Nurpeisova,
Anargul Shaushenova,
Zhazira Mutalova,
Zhandos Zulpykhar,
Maral Ongarbayeva,
Shakizada Niyazbekova,
Alexander Semenov,
Leila Maisigova
Affiliations
Ardak Nurpeisova
Department of Information Systems, Faculty of Computer Systems and Professional Education, S. Seifullin Kazakh Agro Technical University, Nur-Sultan 010000, Kazakhstan
Anargul Shaushenova
Department of Information Systems, Faculty of Computer Systems and Professional Education, S. Seifullin Kazakh Agro Technical University, Nur-Sultan 010000, Kazakhstan
Zhazira Mutalova
Institute of Economics, Information Technologies and Professional Education, Higher School of Information Technologies, Zhangir Khan West Kazakhstan Agrarian Technical University, Uralsk 090000, Kazakhstan
Zhandos Zulpykhar
Department of Computer Science, Faculty Information Technology, L.N. Gumilyov Eurasian National University, Nur-Sultan 010000, Kazakhstan
Maral Ongarbayeva
Department of Information-Communication Technology, Faculty of Natural Sciences, International Taraz Innovative Institute, Taraz 080000, Kazakhstan
Shakizada Niyazbekova
Department of Banking and Monetary Regulation, Financial University under the Government of the Russian Federation, Moscow 125993, Russia
Alexander Semenov
Administration, Moscow Witte University, Moscow 115432, Russia
Leila Maisigova
Department of Accounting, Analysis and Audit, Faculty of Economics, Ingush State University, Magas 386001, Russia
The article analyzes the possibility and rationality of using proctoring technology in remote monitoring of the progress of university students as a tool for identifying a student. Proctoring technology includes face recognition technology. Face recognition belongs to the field of artificial intelligence and biometric recognition. It is a very successful application of image analysis and understanding. To implement the task of determining a person’s face in a video stream, the Python programming language was used with the OpenCV code. Mathematical models of face recognition are also described. These mathematical models are processed during data generation, face analysis and image classification. We considered methods that allow the processes of data generation, image analysis and image classification. We have presented algorithms for solving computer vision problems. We placed 400 photographs of 40 students on the base. The photographs were taken at different angles and used different lighting conditions; there were also interferences such as the presence of a beard, mustache, glasses, hats, etc. When analyzing certain cases of errors, it can be concluded that accuracy decreases primarily due to images with noise and poor lighting quality.
