The Use of Neural Networks in Distance Education Technologies for the Identification of Students

Purpose of the research. The purpose of this research is to study the problems of the features of teaching technologies of modern artificial neural networks for carrying out the procedure of unambiguous authentication of students according to a pre-formed reference base of digital biometric characteristics of the authorized users in the field of distance educational technologies.

In the modern world, artificial neural networks are successfully used in both applied and scientific fields. The problem of authenticating a human personality, implemented using artificial neural networks, finds practical application in solving problems such as the protection of state and corporate information resources, robotics, access control systems, information retrieval, control systems, etc., and is already beginning to find application in the field of distance educational technologies. In March 2021, the Government of the Russian Federation developed a decree on the basis of which higher educational institutions are allowed to use distance learning technologies. Conducting remotely activities of intermediate and final certification, as well as monitoring the current progress of both distance learning students and full-time and part-time students with a temporary transition to distance learning in a pandemic, the problem of identifying the student’s personality arises in order to achieve unambiguous recognition of the authorized users for the purpose of reliable assessment of learning outcomes, which can be solved using modern technologies of artificial neural networks.

Materials and methods. Methods of reviewing scientific literature on the research topic, methods of collecting, structuring and analyzing the information obtained were used as materials and methods.

Research results. The results of the study allow us to draw the following conclusions: to solve the problem of authenticating students in distance education systems it is first necessary to form the actual base of biometric characteristics of the authorized users, which will be compared with the biometric data of the identified users, and for the recognition procedure, the neural network must be trained in advance on special trainers datasets. The identification procedure must be repeated several times during a session to ensure that the identity of the authorized user is verified.

Conclusion. Realizing the set goal to study the problematics of learning technologies of modern artificial neural networks for carrying out the procedure of unambiguous authentication of students according to a pre-formed reference base of digital biometric characteristics of authorized users in the field of distance learning technologies, and relying on the results obtained in the course of generalization and analysis of existing experience and our own studies, the authors identified two independent stages in the algorithm for the implementation of the task of identifying the student’s personality: the formation of a reference base of digital biometric characteristics of authorized users and user authentication according to the previously formed reference base, and also revealed that when training a neural network, it is necessary to take into account a sufficiently large number of different attributes affecting it. With an insufficient number of training sets (datasets), neural networks begin to perceive errors as reliable information, which, as a result, will lead to the need to retrain neural networks. With a sufficiently large number of training sets (dataset), more versions of dependencies and variability appear, which makes it possible to create rather complex machine learning models of neural networks, in which retraining takes the main place.

1. Stefanidi A.F., Lebedev A.A., Matveyev D.V. Investigation of the robustness of algorithms for face recognition in images. Voprosy primeneniya tsifrovoy obrabotki signalov = Questions of the use of digital signal processing. 2018; 8; 4: 174-179. (In Russ.)

2. Voronin I.V., Ziyautdinov V.S., Skudnev D.M. Application of artificial neural networks for biometric identification of a person. Sovremennyye tendentsii razvitiya nauki i proizvodstva. V Mezhdunarodnaya nauchno-prakticheskaya konferentsiya: v 2-kh tomakh = Modern trends in the development of science and production. V International Scientific and Practical Conference: in 2 volumes. West Siberian Scientific Center. 2017: 107-109. (In Russ.)

3. Druzhin D.S., Frolova M.A. Possibility of using neural networks for personal identification. Dni rossiyskoy nauki – 2020. Tezisy dokladov II Otraslevogo nauchnogo foruma = Days of Russian Science 2020. Abstracts of the II Industry Scientific Forum. 2020: 62-65. (In Russ.)

4. Chaban L.N. Teoriya i algoritmy raspoznavaniya obrazov. Uchebnoye posobiye = Theory and algorithms for pattern recognition. Tutorial. Moscow: MIIGAiK; 2004. 70 p. (In Russ.)

5. Istoriya avtomatizirovannogo raspoznavaniya lits= Historyofautomatedfacerecognition[Internet]. Available from: https://deepfakechallenge.com/the-secret-history-of-facial-recognition/. (cited 30.04.2021). (In Russ.)

6. Lucas B.D., Kanade T. An Iterative Image Registration Technique with an Application to Stereo Vision. Proc. 7th Intnl. Joint Conf. on Artificial Intelligence. Vancouver, British Columbia. 1981: 674–679.

7. Ntechlab – dopolnyaya intellect = Ntechlab Complementing Intelligence. Available from: https://ntechlab.ru. (cited 07.05.2021). (In Russ.)

8. Kak Google raspoznayet izobrazheniya = How Google recognizes images [Internet]. Available from: https://policies.google.com/technologies/pattern-recognition?hl=ru. (cited 12.05.2021). (In Russ.)

9. Amazing Uses for Face Recognition [Internet]. Available from: https://www.facefirst.com/blog/amazing-uses-for-face-recognition-facial-recognition-use-cases/. (cited 12.05.2021).

10. Peshkova I. Kto i kak ispol’zuyet tekhnologii raspoznavaniya lits v Rossii = Who and how uses face recognition technology in Russia [Internet]. Available from: https://rb.ru/longread/facialrecognition/. (cited 12.05.2021). (In Russ.)

11. Federal Law of December 31, 2017 N 482FZ «On Amendments to Certain Legislative Acts of the Russian Federation» [Internet]. Available from: http://www.kremlin.ru/acts/bank/42711. (cited 14.05.2021). (In Russ.)

12. Order of the Government of the Russian Federation of June 30, 2018 No. 1322-r «On approval of the consent form for the processing of personal data required for registration of a citizen of the Russian Federation in the unified identification and authentication system, and other information, if such information is provided for by federal laws in this system. , and biometric personal data of a citizen of the Russian Federation in a unified information system of personal data that provides processing, including collection and storage of biometric personal data, their verification and transfer of information on the degree of their compliance with the provided biometric personal data of a citizen of the Russian Federation « [Internet]. Available from: https://www.garant.ru/products/ipo/prime/doc/71879372/. (cited 14.05.2021). (In Russ.)

13. Decree of the Government of the Russian Federation of March 2, 2021 No. 301 «On the approval of the Regulation on the specifics of intermediate certification in the 2021/2022 academic year for educational programs of higher education bachelor’s programs, specialist programs, master’s programs, providing for the use of distance learning technologies that provide identification of a person through a unified information system of personal data, providing processing, including the collection and storage of biometric personal data, their verification and transfer of information on the degree of their compliance with the provided biometric personal data of an individual « [Internet]. Available from: https://www.garant.ru/products/ipo/prime/doc/400313204/. (cited 14.05.2021). (In Russ.)

14. Resolution of the State Committee for Higher Education of the Russian Federation of May 31, 1995 No. 6 «On the state and prospects of creating a unified system of distance education in Russia» [Internet]. Dostup iz sistemy GARANT = Access from the GARANT system. Available from: https://base.garant.ru/181033/. (cited 14.05.2021). (In Russ.)

15. Mal’kova T.V. Formation of the distance learning system in the Russian Federation: to the history of the problem. Nauka i shkola = Science and school. 2009; 1: 63-65. (In Russ.)

16. Protasova A.A., Kozlova O.A. Modern technologies of face identification: research of the algorithm of work and use. Prikladnaya informatika = Applied Informatics. 2020; 15; 2: 131–143. DOI: 10.37791/2687-0649-2020-15-2-131-143. (In Russ.)

17. Tregubenko L.A., Slashchov I.S., Klimenko A.V. Modern approaches to the procedure for identifying persons. Vestnik sovremennykh issledovaniy = Bulletin of modern research. 2019; 3; 6(30): 182–186. (In Russ.)

18. Likhovidov V.N., Gerasimets I.V., Kornyushin P.N. The use of neural networks for the formation of standards in systems of biometric identification of a person. Informatsionnoye protivodeystviye ugrozam terrorizma = Information Counteraction to the Threats of Terrorism. 2006; 7: 52-63. (In Russ.)

19. Zelinov M. A. Studying the work of neural networks: basis neural networks, the use of neural networks in economics. Sbornik trudov mezhdunarodnoy nauchnoy konferentsii «Gumanitarnyye nauki v sovremennom vuze: vchera, segodnya, zavtra» = Proceedings of the international scientific conference «Humanities in a modern university: yesterday, today, tomorrow». 2019: 880–885. (In Russ.)

20. Druzhin D.S., Frolova M.A. Results of the implementation of the personality identification algorithm. Aktual’nyye problemy i puti razvitiya energetiki, tekhniki i tekhnologiy. Sbornik trudov VI Mezhdunarodnoy nauchno-prakticheskoy konferentsii = Actual problems and ways of development of energy, engineering and technology. Proceedings of the VI International Scientific and Practical Conference. 2020: 269-273. (In Russ.)

21. Galushkin A.I. Neyronnyye seti: osnovy teorii = Neural Networks: Foundations of Theory. Moscow: Hotline-Telecom; 2012. 496 p. (In Russ.)

22. Zabashta A.YU., Khokhlov S.A., Skorikova S.A Analysis of existing face recognition algorithms. Sovremennaya nauka: aktual’nyye problemy teorii i praktiki. Seriya: Yestestvennyye i tekhnicheskiye nauki = Modern science: actual problems of theory and practice. Series: Natural and technical sciences. 2017; 7-8: 42-45. (In Russ.)

23. Seledets I.Ye., Borunov D.I., Rychkov V.A. Fundamentals of machine learning. Sinergiya nauk = Synergy of Sciences. 2020; 44: 407–414. (In Russ.)

24. Onufriyeva T.A., Sukhova A.S. Application of neural networks in the development of electronic learning resources. Yuzhno-sibirskiy nauchnyy vestnik = South Siberian Scientific Bulletin. 2020; 6(34): 194–197. DOI: 10.25699/k7125-9820-7795-e. (In Russ.)