Smart Facial Recognition for Tuition Payment Kiosk with Student Verification And Automatic Payment Recording
Keywords:
Facial Recognition, Payment Kiosk, Student Verification, student paymentAbstract
Tuition payment is an important process in educational institutions, but students who mainly use online banking may still need to withdraw or cash out money before they pay in a cash payment. The use of digital payment and cloud-based technologies provide opportunities for more convenient and organized school transactions. Previous studies have also shown the growing use of cloud computing in Philippine higher education and the increasing use of QR-based digital payments.
This study proposes a Smart Facial Recognition for Tuition Payment Kiosk with Student Verification and Automatic Payment Recording as an alternative method of paying tuition and school fees. The proposed system will use facial recognition to verify the student's identity and provide a passcode as an alternative verification method when facial recognition encounters an error. After successful verification, the kiosk will display the student's tuition balance and provide a QR-based payment option through the integration of PayMongo.
Student and payment-related information will be managed through AWS Cloud, while successful transactions will be automatically recorded and a receipt will be printed. The system will be developed and evaluated based on its functionality, student verification, payment processing, transaction recording, and receipt generation. The proposed system is expected to provide students with a more convenient alternative to cash-based tuition payment while helping organize payment transactions through digital processing and cloud-based data management.
References
Zarco, C., Giráldez-Cru, J., Cordón, O., & Liébana-Cabanillas, F. (2024). A comprehensive view of biometric payment in retailing: A complete study from user to expert. Journal of Retailing and Consumer Services, 79, 103789. https://doi.org/10.1016/j.jretconser.2024.103789
T. Sethukarasi, Bharath, S., K. Sujatha, G. Bhavani Bharathi, & S. Saravanakumar. (2023). A smart face biometric verification oriented attendance handling system using artificial intelligence. https://doi.org/10.1109/icses60034.2023.10465453
Yu, T., Wang, C., Bian, Q., & Teoh, A. P. (2024). Acceptance of or resistance to facial recognition payment: A systematic review. Journal of Consumer Behaviour, 23(6), 2933–2951. https://doi.org/10.1002/cb.2385
Ayodeji, Y., Rjoub, H., & Özgit, H. (2022). Achieving sustainable customer loyalty in airports: The role of waiting time satisfaction and self-service technologies. Technology in Society,72, 102106.https://doi.org/10.1016/j.techsoc.2022.102106
Mikhail, R., Archibald, L., Waywaya Taclibon, Temprosa, H. Q., & Austria, Y. D. (2025). ADUPay: A self-service payment kiosk for Adamson University’s recognized student organization. 175–180. https://doi.org/10.1109/micc66164.2025.11210991
Pratama, M. H. A., Rizal, A., & Irawati, I. D. (2025). Advancing secure face recognition payment systems: A systematic literature review. Information, 16(7), 581. https://doi.org/10.3390/info1607058
Moriuchi, E. (2021). An empirical study of consumers’ intention to use biometric facial recognition as a payment method. Psychology & Marketing, 38(10). https://doi.org/10.1002/mar.21495
Liébana-Cabanillas, F., Kalinic, Z., Muñoz-Leiva, F., & Higueras-Castillo, E. (2024). Biometric m-payment systems: A multi-analytical approach to determining use intention. Information & Management, 61(2), 103907. https://doi.org/10.1016/j.im.2023.103907
MELANIE L., B., & CRISTINA D., S. (2024). Campus-based tracking and monitoring system. International Journal of Computer Science and Mobile Computing, 13(8), 70–75. https://doi.org/10.47760/ijcsmc.2024.v13i08.008
Alimboyong, C. R., & Bucjan, M. E. (2021). Cloud computing adoption among state universities and colleges in the Philippines: Issues and challenges. International Journal of Evaluation and Research in Education (IJERE), 10(4), 1455. https://doi.org/10.11591/ijere.v10i4.21526
Musyaffi, A. M., Johari, R. J., Rosnidah, I., Sari, D. A. P., Amal, M. I., Tasyrifania, I., Ayu Pertiwia, S., & Sutanti, F. D. (2021). Digital payment during pandemic: An extension of the unified model of QR code. Academic Journal of Interdisciplinary Studies, 10(6), 213. https://doi.org/10.36941/ajis-2021-0166
Li, C., & Li, H. (2023). Disentangling facial recognition payment service usage behavior: A trust perspective. Telematics and Informatics, 77, 101939. https://doi.org/10.1016/j.tele.2023.101939
Nan, D., Kim, Y., Huang, J., Jung, H. S., & Kim, J. H. (2022). Factors affecting intention of consumers in using face recognition payment in offline markets: An acceptance model for future payment service. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.830152
Calulut, J. M., Lim, R., Revelo, J. D., Sanchez, C. T., Santos, G., & Bigay, L. (2026). FeeTap: A smart payment kiosk with facial recognition for automated student department fee collection. Journal of Interdisciplinary Perspectives, 4(6), 41–50. https://doi.org/10.69569/jip.2026.165
HOME - national privacy commission. (2021, December 29). National Privacy Commission. https://privacy.gov.ph/
Zhou, Y., Wu, N., Hu, B., Zhang, Y., Qiu, J., & Cai, W. (2022). Implementation and performance of face recognition payment system securely encrypted by SM4 algorithm. Information, 13(7), 316. https://doi.org/10.3390/info13070316
Nico Surantha, & Boy Sugijakko. (2024). Lightweight face recognition-based portable attendance system with liveness detection. Internet of Things, 25, 101089–101089. https://doi.org/10.1016/j.iot.2024.101089
Alkaldi, W., AlRossais, N., AlAhmadi, S., AlOmran, H., AlSultan, N., & AlFulaij, N. (2024). MASROOFI: Saudi canteen payment system using face recognition technology. 2024 15th Annual Undergraduate Research Conference on Applied Computing (URC), 1–5. https://doi.org/10.1109/urc62276.2024.10604479
Palash, Md. A. S., Talukder, Md. S., Islam, A. K. M. N., & Bao, Y. (2022). Positive and negative valences, personal innovativeness and intention to use facial recognition for payments. Industrial Management & Data Systems, 122(4), 1081–1108. https://doi.org/10.1108/imds-04-2021-0230
Liu, Y., Yan, W., & Hu, B. (2021). Resistance to facial recognition payment in China: The influence of privacy-related factors. Telecommunications Policy, 45(5), 102155. https://doi.org/10.1016/j.telpol.2021.102155
Zhong, Y., Oh, S., & Moon, H. C. (2021). Service transformation under industry 4.0: Investigating acceptance of facial recognition payment through an extended technology acceptance model. Technology in Society, 64, 101515. https://doi.org/10.1016/j.techsoc.2020.101515
Hu, B., Liu, Y., & Yan, W. (2023). Should I scan my face? The influence of perceived value and trust on Chinese users’ intention to use facial recognition payment. Telematics and Informatics, 78, 101951. https://doi.org/10.1016/j.tele.2023.101951
Kulkarni, S., Rahane, A., Warule, A., Vaidya, S., & Chaudhari, K. (2026). Smart kiosk for automated pay and print with UPI Payment Integration. 2026 2nd International Conference on Computing, Communication and Green Engineering (CCGE), 1–5. https://doi.org/10.1109/ccge67142.2026.11581731
Baquir Tubay, J. (2021). Students’ use of cloud storage in their studies: A case of a private university in the Philippines. Journal of Education and E-Learning Research, 8(1), 16–25. https://doi.org/10.20448/journal.509.2021.81.16.25
Rukhiran, M., Wong-In, S., & Netinant, P. (2023). User acceptance factors related to biometric recognition technologies of examination attendance in higher education: TAM model. Sustainability, 15(4), 3092. https://doi.org/10.3390/su15043092
Hron, M., & Obwegeser, N. (2021). Why and how is Scrum being adapted in practice: A systematic review. Journal of Systems and Software, 183, 111110. https://doi.org/10.1016/j.jss.2021.111110
Edison, H., Wang, X., & Conboy, K. (2022). Comparing methods for large-scale agile software development: A systematic literature review. IEEE Transactions on Software Engineering, 48(2709–2731. https://doi.org/10.1109/TSE.2021.3069039
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Christine Kyle Gamulo, Craesha Leigh Lopez, Neil Santillan, Angel Jevhie Malto, Zenn Paraan (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.