Mobile application for the attendance control of university professors with biometric authentication and geolocation verification
DOI:
https://doi.org/10.51252/rcsi.v4i2.647Keywords:
Amazon Rekognition, geopositioning, fingerprint, smart identification, facial recognitionAbstract
The absence of an effective attendance recording system presents a formidable challenge for educators and educational institutions, leading to disruptions in class schedules, timetables, and apprehensions regarding faculty information security. This study proposes the development of a mobile application for teacher attendance management, integrating biometric authentication and geolocation verification to bolster security in the registration process. Evaluation of the application, conducted with 24 participants at the National University of Trujillo, underscores a 95% accuracy rate in biometric authentication and a notable reduction in registration time, averaging at 32.68 seconds. Moreover, survey results reflect a favorable perception of security among users, consolidating acceptance and trust in the implementation of this pioneering technological solution.
References
Amazon. (2023). Amazon Rekognition Image. AWS. https://aws.amazon.com/es/rekognition/image-features/
Ammour, N., Bazi, Y., & Alajlan, N. (2023). Multimodal Approach for Enhancing Biometric Authentication. Journal of Imaging, 9(9), 168. https://doi.org/10.3390/jimaging9090168 DOI: https://doi.org/10.3390/jimaging9090168
Aza Poveda, S., & Rodriguez Vanegas, J. S. (2020). Sistema de control biométrico de asistencia docente [Universidad Distrital Francisco José de Caldas]. http://hdl.handle.net/11349/28315
Balapour, A., Nikkhah, H. R., & Sabherwal, R. (2020). Mobile application security: Role of perceived privacy as the predictor of security perceptions. International Journal of Information Management, 52, 102063. https://doi.org/10.1016/j.ijinfomgt.2019.102063 DOI: https://doi.org/10.1016/j.ijinfomgt.2019.102063
Bhat, A., Rustagi, S., Purwaha, S. R., & Singhal, S. (2020). Deep-learning based group-photo Attendance System using One Shot Learning. 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), 546–551. https://doi.org/10.1109/ICESC48915.2020.9155755 DOI: https://doi.org/10.1109/ICESC48915.2020.9155755
Bhavsar, K., Shah, D. V., & Gopalan, D. S. (2020). Scrum: An Agile Process Reengineering In Software Engineering. International Journal of Innovative Technology and Exploring Engineering, 9(3), 840–848. https://doi.org/10.35940/ijitee.C8545.019320 DOI: https://doi.org/10.35940/ijitee.C8545.019320
Dudjak, M., & Martinović, G. (2020). An API-first methodology for designing a microservice-based Backend as a Service platform. Information Technology And Control, 49(2), 206–223. https://doi.org/10.5755/j01.itc.49.2.23757 DOI: https://doi.org/10.5755/j01.itc.49.2.23757
Flutter. (2019). Build beautiful native apps in record time. Flutter. https://flutter-website-staging.firebaseapp.com/
Guo, X. (2021). A KNN Classifier for Face Recognition. 2021 International Conference on Communications, Information System and Computer Engineering (CISCE), 292–297. https://doi.org/10.1109/CISCE52179.2021.9445908 DOI: https://doi.org/10.1109/CISCE52179.2021.9445908
ISO/IEC 19794-5:2005. (2005). Information technology — Biometric data interchange formats. International Organization for Standardization. https://www.iso.org/standard/38749.html
ISO/IEC 25010. (2011). Systems and software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — System and software quality models. Organization for Standardization, Technical Committee ISO/IEC JTC 1/SC 7. https://www.iso.org/obp/ui/#iso:std:iso-iec:25010:ed-1:v1:en
ISO/IEC 25040. (2011). Systems and software engineering — Systems and software Quality Requirements and Evaluation (SQuaRE) — Evaluation process. The International Organization for Standardization, Technical Committee ISO/IEC JTC1/SC7. https://www.iso.org/obp/ui/#iso:std:iso-iec:25040:ed-1:v1:en
ISO/IEC JTC1 SC17 WG3. (2018). Portrait Quality: Reference Facial Images for MRTD (Technical Report). International Civil Aviation Organization. https://www.icao.int/Security/FAL/TRIP/Documents/TR - Portrait Quality v1.0.pdf
Kausar, F. (2020). Cancelable Face Template Protection using Transform Features for Cyberworld Security. International Journal of Advanced Computer Science and Applications, 11(1). https://doi.org/10.14569/IJACSA.2020.0110142 DOI: https://doi.org/10.14569/IJACSA.2020.0110142
Kodali, R. K., Panda, A., & Boppana, L. (2023). Attendance System using Amazon Rekognition. TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON), 65–70. https://doi.org/10.1109/TENCON58879.2023.10322521 DOI: https://doi.org/10.1109/TENCON58879.2023.10322521
Kounev, S., Herbst, N., Abad, C. L., Iosup, A., Foster, I., Shenoy, P., Rana, O., & Chien, A. A. (2023). Serverless Computing: What It Is, and What It Is Not? Communications of the ACM, 66(9), 80–92. https://doi.org/10.1145/3587249 DOI: https://doi.org/10.1145/3587249
Leotta, M., Mori, F., & Ribaudo, M. (2023). Evaluating the effectiveness of automatic image captioning for web accessibility. Universal Access in the Information Society, 22(4), 1293–1313. https://doi.org/10.1007/s10209-022-00906-7 DOI: https://doi.org/10.1007/s10209-022-00906-7
Li, L., Chen, C., Pan, L., Zhang, L. Y., Wang, Z., Zhang, J., & Xiang, Y. (2023). A Survey of PPG’s Application in Authentication. Computers & Security, 135, 103488. https://doi.org/10.1016/j.cose.2023.103488 DOI: https://doi.org/10.1016/j.cose.2023.103488
Lovrić, L., Fischer, M., Röderer, N., & Wünsch, A. (2023). Evaluation of the Cross-Platform Framework Flutter Using the Example of a Cancer Counselling App. Proceedings of the 9th International Conference on Information and Communication Technologies for Ageing Well and E-Health, 135–142. https://doi.org/10.5220/0011824500003476 DOI: https://doi.org/10.5220/0011824500003476
Moral, P. (2021). Sistemas de geolocalización, control del trabajador y facultad disciplinaria empresarial [Universidad de Valladolid]. https://uvadoc.uva.es/handle/10324/50965
Nakisa, B., Ansarizadeh, F., Oommen, P., & Kumar, R. (2023). Using an extended technology acceptance model to investigate facial authentication. Telematics and Informatics Reports, 12, 100099. https://doi.org/10.1016/j.teler.2023.100099 DOI: https://doi.org/10.1016/j.teler.2023.100099
Novoa, P., Reyes, J., & Cedeño, J. (2019). Aplicación móvil inteligente para asistir el registro de actividades académicas en sistemas biométricos: una experiencia universitaria en el Ecuador. Revista Científica de La Universidad de Cienfuegos, 11(2), 55–60. https://rus.ucf.edu.cu/index.php/rus/article/view/1150
Padilha, R., Andaló, F. A., Bertocco, G., Almeida, W. R., Dias, W., Resek, T., Torres, R. da S., Wainer, J., & Rocha, A. (2020). Two‐tiered face verification with low‐memory footprint for mobile devices. IET Biometrics, 9(5), 205–215. https://doi.org/10.1049/iet-bmt.2020.0031 DOI: https://doi.org/10.1049/iet-bmt.2020.0031
Saadon, J. R., Yang, F., Burgert, R., Mohammad, S., Gammel, T., Sepe, M., Rafailovich, M., Mikell, C. B., Polak, P., & Mofakham, S. (2023). Real-time emotion detection by quantitative facial motion analysis. PLOS ONE, 18(3), e0282730. https://doi.org/10.1371/journal.pone.0282730 DOI: https://doi.org/10.1371/journal.pone.0282730
Salvatierra, G. (2018). Desarrollo de un sistema de control de asistencia estudiantil mediante reconocimiento facial [Universidad Internacional de la Rioja]. https://reunir.unir.net/handle/123456789/7425
Sandhya, N., Vijaya Saraswathi, R., Preethi, P., Aarti Chowdary, K., Rishitha, M., & Sai Vaishnavi, V. (2022). Smart Attendance System Using Speech Recognition. 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), 144–149. https://doi.org/10.1109/ICSSIT53264.2022.9716261 DOI: https://doi.org/10.1109/ICSSIT53264.2022.9716261
Sang, J., Lei, Z., & Li, S. Z. (2009). Face Image Quality Evaluation for ISO/IEC Standards 19794-5 and 29794-5 (pp. 229–238). https://doi.org/10.1007/978-3-642-01793-3_24 DOI: https://doi.org/10.1007/978-3-642-01793-3_24
Silvelo, A. (2019). Sistema de autenticación biométrica basado en el análisis del comportamiento mediante interacción por pantalla táctil y sensores de movimiento [Universidad de La Coruña]. http://hdl.handle.net/2183/24560
Soewito, B., Gaol, F. L., Simanjuntak, E., & Gunawan, F. E. (2016). Smart mobile attendance system using voice recognition and fingerprint on smartphone. 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA), 175–180. https://doi.org/10.1109/ISITIA.2016.7828654 DOI: https://doi.org/10.1109/ISITIA.2016.7828654
Sulla, T. (2022). Sistema biométrico basado en aplicaciones móviles para el control de asistencia de estudiantes del Instituto Superior Tecnológico Americana del Cusco [Universidad de Guayaquil]. http://repositorio.ug.edu.ec/handle/redug/30756
Supabase. (2023). The Open Source Firebase Alternative. Supabase. https://supabase.com/
Tee, T. X., & Khoo, H. K. (2020). Facial Recognition using Enhanced Facial Features k-Nearest Neighbor (k-NN) for Attendance System. Proceedings of the 2020 2nd International Conference on Information Technology and Computer Communications, 14–18. https://doi.org/10.1145/3417473.3417475 DOI: https://doi.org/10.1145/3417473.3417475
Torres, E. (2019). Implementación De Un Sistema De Control De Asistencia Con Código Qr Para La Institución Educativa Ricardo Palma – Carhuaz; 2019 [Universidad Católica Los Ángeles Chimbote]. http://repositorio.uladech.edu.pe/handle/20.500.13032/13800
Valverde, M. (2018). Desarrollo de una aplicación móvil android para la Empresa Righttek S.A. como aporte a los controles de localización y registro de ubicación del personal de soporte a usuarios [Universidad César Vallejo]. https://hdl.handle.net/20.500.12692/87748
Vardakis, G., Tsamis, G., Koutsaki, E., Haridimos, K., & Papadakis, N. (2022). Smart Home: Deep Learning as a Method for Machine Learning in Recognition of Face, Silhouette and Human Activity in the Service of a Safe Home. Electronics, 11(10), 1622. https://doi.org/10.3390/electronics11101622 DOI: https://doi.org/10.3390/electronics11101622
Wasilewski, K., & Zabierowski, W. (2021). A Comparison of Java, Flutter and Kotlin/Native Technologies for Sensor Data-Driven Applications. Sensors, 21(10), 3324. https://doi.org/10.3390/s21103324 DOI: https://doi.org/10.3390/s21103324
Zambrano-Vega, C., Oviedo, B., & Moncayo Carreño, O. (2020). Assessing the Performance of a Biometric Mobile Application for Workdays Registration (pp. 1004–1015). https://doi.org/10.1007/978-3-030-12385-7_68 DOI: https://doi.org/10.1007/978-3-030-12385-7_68
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Copyright (c) 2024 Bruno Adrián Montañez-Díaz, Willy Francisco García-Gutiérrez, Raphael Andre Prieto-Pastor, Alberto Mendoza-De-los-Santos
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