Utilizing Real – Time Face Recognition Based Bio-Metric System for Online Transaction
DOI:
https://doi.org/10.32628/CSEIT24103108Keywords:
Online Banking, Face Recognition Technology, Grassmann Learning Algorithm, Real-Time Authentication, Funds Transfer, User-Friendly Interface, Security Notifications, Identity VerificationAbstract
A crucial component of contemporary banking is now online banking. Due to the present password- based authentication paradigm’s inadequacies in terms of efficiency and robust, as well as their suspectibility to automated attacks, several attempts are successful in gaining access to social network accounts. The easiest solution is to add more identifying features, like one-time PIN numbers that are created by the user’s own device(like a smart phone) or sent to them via SMS to the single factor(Password-based) authentication procedure. With the help of this technology, client’s identities may be instantly and conveniently verified. The goal of this project is to create an online banking system that authenticates customer’s using real-time facial recognition technology. The system will be made to offer a safe and convenient user interface that enables users to perform financial operation like bill payment, money transfers, and balance queries. A facial recognition algorithm, such Grassmann learning, which can record and evaluate customer’s facial traits in real time, will be included into the system. To confirm customer’s identification, the algorithm will match the customer’s facial traits with those in the bank’s database. The technology would give users a safe and convenient interface to conduct real-time banking transactions. Notifications about banking amount transactions are sent to the user in this suggested netbanking application.
Downloads
References
Md Golam Mohiuddin, Avirup Chowdhury, Amogh Banerjee, Shalini Singh, Indrajit Das, Ria Das, and Amogh Banerjee. "Design and implementation of eye pupil movement based PIN authentication system." 2020 IEEE VLSI DCS (VLSI DEVICE CIRCUIT AND SYSTEM), pages 1-6. IEEE, 2020.
Jenny Domashova and Elena Kripak. "Identification of non-typical international transactions on bank cards of individuals using machine learning methods." 178–183 in Procedia Computer Science 190 (2021). DOI: https://doi.org/10.1016/j.procs.2021.06.023
Natalia Mikhailina, Jenny Domashova, and others. "Usage of machine learning methods for early detection of money laundering schemes." 184–192 in Procedia Computer Science 190 (2021). DOI: https://doi.org/10.1016/j.procs.2021.06.033
Alessio Merlo, Guerar, Meriem, Luca Verderame, Francesco Palmieri, and Mauro Migliardi. "Securing PIN‐based authentication in smartwatches with just two gestures." Practice and Experience with Concurrency and Computation 32, no. 18 (2020): e5549. DOI: https://doi.org/10.1002/cpe.5549
Kabir, M. Monjirul, Nasimul Hasan, Tanjil Ahmed Ovi, Md Khalid Hassan Tahmid, and Victor Stany Rozario, "Enhancing Smartphone lock security using vibration enabled randomly positioned numbers." In the International Conference on Computing Advancements Proceedings, 2020, pp. 1–7. DOI: https://doi.org/10.1145/3377049.3377099
Kangbin Yim, Sun-Young Lee, Kyungroul Lee, and Lee. "Classification and Analysis of Security Techniques for the User Terminal Area in the Internet Banking Service." 2020, Security and Communication Networks, 1-16. DOI: https://doi.org/10.1155/2020/7672941
Mohamed Elhoseny, Khaled, and Riad. "A Blockchain-based key-revocation access control for open banking." Mobile Computing and Wireless Communications 2022 (2022). DOI: https://doi.org/10.1155/2022/3200891
Rtayli, Naoufal, and Nourdine Enneya, "Selection features and support vector machine for credit card risk identification." 46 (2020) Procedia Manufacturing: 941-948. DOI: https://doi.org/10.1016/j.promfg.2020.05.012
Chinmaya Gayathri, B. Aishwarya, Gautam Pradyumna, Hari Krishna, and SM. "Development of personal identification number authorization algorithm using real-time eye tracking & dynamic keypad generation."Pages 1-6 of the Sixth International Conference on Convergence in Technology (I2CT), 2021.IEEE, 2021.
Veena, K., Meena, K., Ramya Kuppusamy, Arun Radhakrishnan, and Yuvaraja Teekaraman. "C SVM classification and KNN techniques for cyber-crime detection." 2022: 1–9. Wireless Communications and Mobile Computing. DOI: https://doi.org/10.1155/2022/3640017
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.