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5 Oct 2021
On October 5, 2021, a business meeting was held between representatives of the EPAM Systems IT Company Denis Grinev and Sergey Garashchuk with the Rector of the State University “Odessa Polytechnic” Gennadii Alexandrovich Oborskiy
17 Sept 2021
International Summer School
15 July 2021
Until November 1, 2021, enrollment in the double degree program of Slovakia 2ouble Degree is carried out.
AUTOMATED STUDENT ATTENDANCE MONITORING SYSTEM IN CLASSROOM BASED ON CONVOLUTIONAL NEURAL NETWORKS
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
Nhan Cach Dang
, postgraduate student
( email@example.com )
Quoc Tuan Le
, Ph.D, Senior Lecturer
( firstname.lastname@example.org )
Svetlana G. Antoshchuk
, Dr. of Tech. Sciences, Professor
( email@example.com )
The Vinh Tran
, Dr. of Tech. Sciences, Professor
( firstname.lastname@example.org )
Thi Khanh Tien Nguyen
, Ph.D, Senior Lecturer
( email@example.com )
biometric face recognition; convolutional neural network; deep learning; computer vision; face detection; Haar cascade; image processing; face dataset
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Received after revision 10.09.2020
Vol. 3 № 3, 2020
9 Oct 2021
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