MODEL OF THE FACIAL RECOGNITION PROCEDURE MODEL AND THE IRIS OF THE EYE DURING BIOMETRIC AUTHENTICATION OF PERSONNEL OF CRITICAL INFRASTRUCTURE FACILITIES USING NEURAL NETWORK TOOLS.
DOI:
https://doi.org/10.18372/2225-5036.30.19247Keywords:
biometric authentication, person recognition, face image, iris of the eye, neural network, critical infrastructure objectAbstract
The problematics of the article is related to increasing the effectiveness of biometric authentication systems for personnel of critical infrastructure facilities. It is shown that the prospects of increasing efficiency should be correlated with the improvement of neural network tools used in the process of biometric authentication. As a result of the conducted research, a modular neural network model was developed that provides effective authentication of personnel based on the image of the face and the iris of the eye at a critical infrastructure facility, taking into account the need to recognize spoofing attacks and promptly update data on the list of legitimate personnel representatives of the facility. The novelty of the proposed modular neural network model consists in the application of the author's variants of neural networks, which allow to realize the recognition of the emotional state of the registered person, the recognition of spoofing attacks based on the naturalness of emotions and images of background objects characteristic of specific conditions of video registration, and the recognition of a person by comparing the test image faces with images of the faces of legitimate personnel, which makes it possible to quickly respond to a change in the list of legitimate personnel representatives of a critical infrastructure object without the need to retrain the model.
References
Ahmad Sabri N.I., Setumin S. One-Shot Learning for Facial Sketch Recognition using the Siamese Convolutional Neural Network. 2021 IEEE 11th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), Penang, Malaysia, 2021, pp. 307-312. DOI: 10.1109/ISCAIE51753.2021.9431773.
Daniel N., Anitha A. Texture and quality analysis for face spoofing detection. Computers & Electrical Engineering, Volume 94, 2021, 107293, ISSN 0045-7906. DOI: 10.1016/j.compeleceng.2021.107293.
Edmunds T., Caplier A. Motion-based countermeasure against photo and video spoofing attacks in face recognition. Journal of Visual Communication and Image Representation, Volume 50, 2018, Pages 314-332, ISSN 1047-3203. DOI: 10.1016/j.jvcir.2017.12.004.
Hamdani N., Bousahba N., Bousbai A., Braikia A. Face Detection and Recognition Using Siamese Neural Network. International Journal of Computing and Digital System (Jāmiʻat al-Baḥrayn. Markaz al-Nashr al-ʻIlmī), 2023, Vol. 14, No. 1, pp. 889–897. DOI: 10.12785/ijcds/140169.
Hangaragi S., Singh T., Neelima N. Face Detection and Recognition Using Face Mesh and Deep Neural Network. Procedia Computer Science, Volume 218, 2023, pp. 741-749, ISSN 1877-0509. DOI: 10.1016/j.procs.2023.01.054.
Kumar C.R., Saranya N., Priyadharshini M., Gilchrist E.D., Rahman M.K. Face recognition using CNN and siamese network. Measurement: Sensors, Vol. 27, 2023, 100800, ISSN 2665-9174.
Li L., Correia P.L., Hadid A. Face recognition under spoofing attacks: countermeasures and research directions. IET Biometrics, 2018, Vol. 7, pp. 3-14.
Ma Y., Wu L., Li Z., Liu F. A novel face presentation attack detection scheme based on multi-regional convolutional neural networks. Pattern Recognition Letters, Volume 131, 2020, pp. 261-267, ISSN 0167-8655.
Muthukumaran B., Harshavarthanan L., Dhyaneshwar S., Sharief M.Z. Face and Iris based Human Authentication using Deep Learning. 2023. 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, 2023, pp. 841-846.
Osa E., Orukpe P.E., Iruansi U. Design and implementation of a deep neural network approach for intrusion detection systems. e-Prime - Advances in Electrical Engineering, Electronics and Energy. Vol. 7, 2024, 100434, ISSN 2772-6711.
Perdana R.N., Ardiyanto I., Nugroho H.A. A Review on Face Anti-Spoofing. IJITEE (International Journal of Information Technology and Electrical Engineering), 2021. Vol. 5, No. 1, pp. 29.
Pranav K.B., Manikandan J. Design and Evaluation of a Real-Time Face Recognition System using Convolutional Neural Networks. Procedia Computer Science. Vol. 171, 2020, pp. 1651-1659, ISSN 1877-0509.
Tran H.N., Phan P.H., Nguyen K.H., et al. Augmentation-Enhanced Deep Learning for Face Detection and Emotion Recognition in Elderly Care Robots. PREPRINT, 07 February 2024, Research Square.
Wang Y., Tan T., Jain A.K. Combining Face and Iris Biometrics for Identity Verification. In: Kittler J., Nixon M.S. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2003, Lecture Notes in Computer Science. Vol. 2688. Springer, Berlin, Heidelberg. 2003.
Yergesh A.K. Development of an advanced biometric authentication system using iris recognition based on a convolutional neural network. Herald of Science. Vol. 2, no. 5 (74), 2024, pp. 615-625.
Бушуєв С.Д., Кулаков Ю.О., Терейковська Л.О., Терейковський І.А., Терейковський О.І. Нейромережева модель детектування кордонів обличчя людини. Науково-технічний збірник «Управління розвитком складних систем» Київського національного університету будівництва і архітектури, 2022. Вип. 51, С. 5-11.
Корченко О.Г., Терейковський О.І. Аналіз та оцінювання засобів біометричної аутентифікації за зображенням обличчя та райдужної оболонки ока персоналу об’єктів критичної інфраструктури. Кібербезпека: освіта, наука, техніка, №1(21), 2023. С. 136-148.
Корченко О.Г., Терейковський О.І. Модель процедури розпізнавання особи за зображенням обличчя та райдужною оболонкою ока при біометричній автентифікації персоналу об’єктів критичної інфраструктури з застосуванням нейромережевих засобів. Захист інформації, №1(26), 2024. С. 157-170.
Терейковський І.А., Заріцький О.В., Терейковська Л.О., Погорелов В.В. Метод розробки архітектури глибокої нейронної мережі, призначеної для розпізнавання комп’ютерних вірусів. Захист інформації, Том 20, № 3 (2018). С. 188-199.
