D. Muratuly, N. Denissova, Y. Krak, К. Apayev


This article considers the relevant problem of biometric authentication of students in higher educational institutions. The authors present the results of using a turnstile system with a face recognition terminal, with the ability to provide unique biometric data in real time. The study was conducted among students of the D. Serikbayev East Kazakhstan Technical University, Ust-Kamenogorsk, Kazakhstan. The article presents the results of studies of one of the biometric methods of personality recognition. In this method, the process of proving and verifying the identity of the person can be carried out through the presentation by the user of his biometric image. The processing results are sorted and compared with typical images from the database. With its positive decision, the developed software issues the results of biometric authentication of a person who presented himself in front of a digital scanner. The applied value of the results of the work lies in the possibility of using them in the field of education, and various industries to make a decision on providing access to information resources. In the course of the study, a technology was developed to provide biometric authentication processes for university students. Domestic and foreign scientists who have made a significant contribution to the development of methods for processing facial images are noted. A review of biometric methods of recognition is carried out, and tools for electronic authentication and modern information security systems are described. Factors that significantly affect the probability of correct recognition of students’ faces are determined. The analysis of ways to increase the probability of correct recognition of students by the image of the face is carried out.

Ключевые слова

Biometric authentication, face recognition

Полный текст:

PDF (English)


H. Farouk E.-S., Al, K. Alghatani T. A., (2013), ‘The impact of cloud computing technologies in

E-learning’, International Journal of Emerging Technologies in Learning (iJET), vol. 8,89, pp. 37–43.

Moini A, Madni AM., (2009), ‘Leveraging biometrics for user authentication in online learning: A systems

perspective’, IEEE Systems Journal, 3(4), pp.469–476, https://doi: 10.1109/JSYST.2009.2038957.

Kashyap R., (2019), ‘Biometric authentication techniques and E-learning’, Biometric Authentication in

Online Learning Environments, IGI Global, Hershey, PA, USA, pp.236-265, https://doi:10.4018/978-


Kotwal D.V., Bhadke S.R., Gunjal A. S., (2016), ‘Online examination system’, International Research

Journal of Engineering and Technology (IRJET), vol. 3, no. 1, pp. 115–117.

Okada A., Whitelock D., Holmes W., (2019), ‘E-authentication for online assessment: a mixedmethod study’, British Journal of Educational Technology, vol. 50, no. 2, pp. 861–875.

Atoum Y., Chen L., Liu A. X., Hsu S. D., Liu X., (2016), ‘Automated online exam proctoring’, IEEE

Transactions on Multimedia, vol. 99.

Kausar S., Huahu X., Ullah A., (2020), ‘Fog-assisted secure data exchange for examination and

testing in E-learning System’, Mobile Networks and Applications, pp. 1–17.

Cao Q., Shen L., Xie W., Parkhi O., Zisserman A., (2018), ‘VGGFace2: A dataset for recognising

faces across pose and age’, in Proc. 13th IEEE International Conference on Automatic Face & Gesture

Recognition, pp. 67-74.

Dang K., Sharma S., (2017), ‘Review and comparison of face detection algorithms’, 7th International

Conference on Cloud Computing, Data Science & Engineering - Confluence, Noida, India, Jan. 12-13.

Dundar A., Jin J., Martini B., Culurciello E., (2017), ‘Embedded streaming deep neural networks

accelerator with applications’, IEEE Transactions on Neural Networks and Learning Systems, vol. 28,

no. 7, pp. 1572–1583.

Pranav K.B; Manikandan J., (2020), ‘Design and Evaluation of a Real-Time Face Recognition System

using Convolutional Neural Networks’, Procedia Computer Scienc, Volume 171, pp. 1651-1659,

Liao S., Jain A. K., Li S.Z., (2016), ‘A fast and accurate unconstrained face detector’, IEEE Transactions

on Pattern Analysis and Machine Intelligence, vol. 38, no. 2, pp. 211–223.

Zhao K., Xu J., Cheng M., (2019), ‘Deep face recognition via exclusive regularization’, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1136–1144, https://doi.


Schroff F., Kalenichenko D., Philbin J., (2015), ‘A unified embedding for face recognition and

clustering’, In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 815–

Kumar A.; Kaur A.; Kumar M., (2020), ‘Review Paper on Face Detection Techniques’, International

journal of engineering research & technology, 8, pp.32–33.

Rusyn B.P., Lutsyk O.A., Kosarevych R.Y., (2021), ‘Evaluating the informativity of training sample for

classification of images by deep learning methods’, Cybernetics and Systems Analysis, Vol. 57, N6,


Salamh A. B. S., & Akyüz, H. (2022), A New Deep Learning Model for Face Recognition and Registration in Distance Learning, International Journal of Emerging Technologies in Learning (iJET), 17(12),




  • Ссылки не определены.

(P): 2707-9031
(E): 2707-904X

Articles are open access under the Creative Commons License  

Бизнес-центр EXPO, блок C.1.
Казахстан, 010000