Home⇛Journal of Interdisciplinary Perspectives⇛vol. 4 no. 6 (2026)

FeeTap: A Smart Payment Kiosk with Facial Recognition for Automated Student Department Fee Collection

Joshua Miguel C. Calulut | Rufaida A. Lim | John Dexter V. Revelo | Caroline Therese G. Sanchez | Gajil J. Santos | Lemuel S. Bigay

Discipline: information systems

 

Abstract:

Traditional manual fee collection at academic institutions often leads to administrative bottlenecks and prolonged wait times. To address these inefficiencies, this study developed and evaluated FeeTap, an automated self-service payment kiosk prototype integrated with biometric facial verification. Implemented using a Raspberry Pi 4B architecture and Python’s face_recognition library, FeeTap features a multi-denominational currency acceptor, automated change dispensing, and a dedicated mobile application for real-time transaction monitoring. Utilizing a quasiexperimental design, the researchers conducted preliminary performance testing with a purposive sample of computer engineering students to assess biometric accuracy and operational throughput. Results indicated that the facial recognition module achieved a 72.92% cumulative success rate within a three-attempt authentication protocol. The remaining 27.08% composite failure rate consisted of both recognition timeouts and misidentifications, suggesting sensitivity to environmental micro-variations. Furthermore, a paired-samples t-test of successfully authenticated participants (n = 35) revealed a statistically significant difference between the FeeTap and manual methods in transaction times (t(34) = 12.59, p < .001). For this subgroup, the FeeTap system demonstrated a 44.77% reduction in mean transaction time, from 121.06 seconds to 66.86 seconds, including recognition latency. While a strong positive correlation (r = .77) was observed, the high failure rate and preliminary accuracy suggest that the system currently functions as a proofof- concept. These findings indicate that while FeeTap offers a promising alternative to manual transactions, further optimization of the biometric layer and failure-handling protocols is required for campus-wide deployment.



References:

  1. Adigun, A.A., Abolarinwa, M.O., Ojo, O.E., Oladimeji, A.I., & Bakare, O.S. (2024). Enhanced local binary pattern algorithm for facial recognition using Chinese remainder theorem. Dutse Journal of Pure and Applied Sciences (DUJOPAS), 10(1c), 255–262. https://doi.org/10.4314/dujopas.v10i1c.24
  2. Bellalem, F., Mirza, C., & Mirza, H. (2023). Ethical considerations in qualitative research: Summary guidelines for novice social science researchers. Social Studies and Research Journal. 11(1), 441–449. https://tinyurl.com/yyrhv9j6
  3. Borbon, N. (2024). Kiosk revolution: The transformation of fast food in the Philippines. In: A. Garg (Ed.), Technological Innovations in the Food Service Industry (pp. 69-80). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7683-6.ch004
  4. Capili, B., & Anastasi, J. (2024). An introduction to the quasi-experimental design (nonrandomized design). American Journal of Nursing, 124(11), 50–52. https://doi.org/10.1097/01.NAJ.0001081740.74815.20
  5. Chicco, D., Sichenze, A., & Jurman, G. (2025). A simple guide to the use of Student’s t-test, Mann-Whitney U test, Chi-squared test, and Kruskal-Wallis test in biostatistics. BioData Mining, 18(1), 56. https://doi.org/10.1186/s13040-025-00465-6
  6. Choi, H.-s.C., Flaherty, J., Liang, L.J., & Rastegar, N. (2021). The adoption of self-service kiosks in quick-service restaurants. European Journal of Tourism Research, 27, 2709. https://doi.org/10.54055/ejtr.v27i.2139
  7. Čižiūnienė, K., Prokopovič, M., Zaranka, J., & Matijošius, J. (2024). Biometric breakthroughs for sustainable travel: Transforming public transportation through secure identification. Sustainability, 16(12), 5071. https://doi.org/10.3390/su16125071
  8. De Jesus, F., Nocum, C.L., Punzal, C., & Villanueva, F. (2024). User interface (UI) on self-service kiosks’ machine in fast-food industry in Nueva Ecija, Philippines: Its’ correlation towards customers’ experiences. Edelweiss Applied Science and Technology, 8(6), 3300–3312. https://doi.org/10.55214/25768484.v8i6.2710
  9. El Omda, S., & Sergeant, S. (2024). Standard deviation. In: StatPearls. Treasure Island (FL): StatPearls Publishing. https://tinyurl.com/4fnjd2d5
  10. Gao, W., Jiang, N., & Guo, Q. (2025). Facial recognition payment is cool: Coolness, inspiration, and customer continuance intention to use facial recognition payment. Financial Innovation, 11(1), 31. https://doi.org/10.1186/s40854-024-00697-1
  11. Hirsch, I. (2026). Rethinking traditional payments in a virtual-first world. WEX. https://tinyurl.com/nh6vwkdt
  12. Mutalova, Z., Shaushenova, A., Nurpeisova, A., Shayea, I., & Ongarbayeva, M. (2025). Adaptive image optimization for difficult lighting conditions in face recognition. International Journal of Innovative Research and Scientific Studies, 8(3), 836–845. https://doi.org/10.53894/ijirss.v8i3.6618
  13. Oroceo, P., Kim, J.-I., Caliwag, E.M.F., Kim, S.-H., & Lim, W. (2022). Optimizing face recognition inference with a collaborative edge–cloud network. Sensors, 22(21), 8371. https://doi.org/10.3390/s22218371
  14. Orostegui, C.M., Luna, A.N., García, A.M., & Ariza, C.A. (2021). A low-cost Raspberry Pi-based system for facial recognition. Ingeniería y Ciencia, 17(34), 77–95. https://doi.org/10.17230/ingciencia.17.34.4
  15. Pecolt, S., Błażejewski, A., Królikowski, T., Maciejewski, I., Gierula, K., & Glowinski, S. (2025). Personal identification using embedded Raspberry Pi-based face recognition systems. Applied Sciences, 15(2), 887. https://doi.org/10.3390/app15020887
  16. Putri, L., Rezani, M., & Hermina, D. (2025). Correlational research design. Jurnal Riset Multidisiplin Edukasi, 2(6), 306–317. https://doi.org/10.71282/jurmie.v2i6.456
  17. Rija, R., Muttasher, G., & Al-Araji, A. (2022). Payment systems based on face recognition: A survey. Journal of Optoelectronics Laser, 41(5), 563–571. https://tinyurl.com/4amr5jsy
  18. Sundar, A., & Singh, K.J. (2024). A comprehensive review on secure biometric-based continuous authentication and user profiling. IEEE Access, 12(1), 82996–83021. https://doi.org/10.1109/ACCESS.2024.3411783
  19. Tamondong, D.R., Cabus, E.A., Echalar, A.R., Santos, J.J., & Bernarte, R. (2022). Assessment of self-service kiosk as food service tool among customers in top fast-food chains in Manila. Lathala: The Lyceum of the Philippines University Research Journal. https://tinyurl.com/4uv6n4ya
  20. Varadharajan, V., Rajasekaran, S., & Mohan Kumar, P. (2021). Prediction of crop yield and cost by finding best accuracy using machine learning approach. International Journal of Computer Science and Information Technology Research, 8(3), 253–263. https://.com/yc2bujmu
  21. Wisastra, A., Ardianyah, A.E., Hermanto, B.A., & Luhukay, D. (2024). The influence of self-service kiosks on customer experience in retail stores. 2024 International Electronics Symposium (IES), 365–370, https://doi.org/10.1109/IES63037.2024.10665819
  22. Zamora, E., Dela Cruz, R., Martinez, G., Villamor, D.F., & Ybanez, S. (2024). Level of customer satisfaction with using technology-based self-service kiosks in fastfood industries along Valenzuela City. Ascendens Asia Singapore – Bestlink College of the Philippines Journal of Multidisciplinary Research, 5(1). https://tinyurl.com/5n7a9289

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