Аutomated student attendance monitoring system in classroom based on convolutional neural networks

Abstract

Attending classes by students is associated with the assimilation of educational material by students and the ability to plan and organize
activities. However, at present in educational institutions, as a rule, student attendance is recorded manually. Activities are performed
frequently and repeatedly, thus wasting instructors' study time. Additionally, the face is one of the most widely used biometric
characteristics for personal identification so an automated attendance system using face recognition has been proposed. In recent years,
convolutional neural networks (CNN) have become the dominant deep le11arning method for face recognition. In this article, the
features of building an automated student attendance system by biometric face recognition using the convolution neural network model
has been discussed. Analyzed and solved the main tasks that arise when building an automated student attendance monitoring system:
creating a dataset of students' face images; building and training a biometric face recognition model; face recognition from the camera
and registration in the database; extension to the face image dataset. The use of the capabilities of the Python and OpenCV libraries is
shown. The conducted testing of the accuracy of the developed CNN model of biometric face recognition showed good results – the
overall accuracy score is not less than 0.75. The developed automated student attendance monitoring system in classrooms can be used
to determine student attendance in different forms of the educational process. Its implementation will significantly reduce the
monitoring time and reduce the number of errors in maintaining attendance logs. The introduction of an automated attendance
monitoring system will significantly improve the organization of the educational process to ensure its quality.