The Study of Mathematical Models and Algorithms for Face Recognition in Images Using Python in Proctoring System

• Published in: Computation Vol. 10; no. 8; p. 136
• Main Authors: Nurpeisova, Ardak, Shaushenova, Anargul, Mutalova, Zhazira, Zulpykhar, Zhandos, Ongarbayeva, Maral, Niyazbekova, Shakizada, Semenov, Alexander, Maisigova, Leila
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2022
• Subjects: Accuracy; AI-based AEPS (artificial intelligence-based automated exam proctoring systems); Airports; algorithm; Algorithms; Artificial intelligence; Automation; Colleges & universities; Computer vision; Data analysis; Data compression; Distance learning; Face recognition; Facial recognition technology; Fraud prevention; Higher education; Image analysis; Image classification; Image processing; Image quality; Information technology; Lighting; Machine learning; Mathematical analysis; mathematical model; Mathematical models; Methods; Neural networks; Object recognition; Online education; Outerwear; Pattern recognition; person detection; proctoring systems; Programming languages; Python; Python (Programming language); Remote monitoring; Students; Support vector machines; Video data; Kazakhstan
• ISSN: 2079-3197; 2079-3197
• DOI: 10.3390/computation10080136

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.