Realization of a WebGIS for Remote Control of Biometric Sensors for Facial Recognition of Permanent Staff in an Educational Environment: The Case of the Ministry of National Education in Conakry, Guinea
- 1 Centre Universitaire de Recherche et d’Application en Télédétection (CURAT), Université Félix Houphouët-Boigny, Abidjan, Côte d’Ivoire
- 2 Centre Universitaire de Recherche et d’Application en Télédétection (CURAT), Université Félix Houphouët-Boigny, Abidjan, Côte d’Ivoire
- 3 Institut de Recherche sur les Énergies Nouvelles (IREN), Université Nangui Abrogoua, Abidjan, Côte d’Ivoire
- 4 Centre Universitaire de Recherche et d’Application en Télédétection (CURAT), Université Félix Houphouët-Boigny, Abidjan, Côte d’Ivoire
Abstract
Initially focused on the earth sciences and mining resources, remote sensing is now finding increasing applications, notably in human resources management in public administration. This study proposes a WebGIS for real-time tracking using geolocated biometric sensors for facial recognition, with a view to optimizing the attendance management of MENA teachers in Conakry. The system is based on an integrated architecture combining open-source facial recognition sensors, a PostgreSQL/PostGIS database, a QGIS Server, and a Lizmap Web Client interface. Each sensor, installed in a school, automatically detects the teachers initially enrolled and records their clocking-in times. Data is stored locally and then transmitted to the central server via the MQTT protocol, with GSM/GPRS or LoRa relay in the event of failure. This infrastructure enables attendance to be mapped and controlled remotely, promoting transparency and administrative efficiency. Analysis of facial recognition clocking-in data, collected in nine MENA schools in Conakry for a total of 334 teachers, reveals an overall weekly attendance rate of 80.7%, i . e ., 1,348 attendances out of 1,670 expected. Of these, 73.5% were on schedule, while 26.5% showed irregularities (late arrivals, early departures, or both). These discrepancies are mainly due to rush-hour traffic jams, aggravated by the fact that, for economic reasons, most teachers live in the suburbs. As for absences (19.3%), the main causes identified are: socio-political problems (70%), social events (22%), health reasons (7%), and 1% unjustified. A notable anomaly was observed on the 2 nd day of collection with 68% absences, linked to a political demonstration by opposition parties. The integration of these data into a WebGIS system enables dynamic, geographical visualization of teacher presence, promoting better workforce management and decision-making within the Ministry.
- MFPREMA: Ministère de la Fonction Publique de la Réforme de l’État et de la Modernisation de l’Administration (2015) Contrôle des fonctionnaires à par des pointages électroniques. https://www.guinee7.com/2015/11/12/sekou-kouroumah-ministre-de-la-fonction-publique-ce-qui-echappe-a-notre-administration-cest-le-controle-laisser-aller-le-laxisme/
- Guinee7 (2015) Le pointage électronique, une réalité dans l’administration publique. https://www.guinee7.com/2015/03/25/le-pointage-electronique-une-realite-dans-ladministration-publique/
- WeAreTech Africa (2024) Cameroon Bets on Biometrics to Monitor Public Sector Attendance. https://www.wearetech.africa/en/fils-uk/news/tech/cameroon-bets-on-biometrics-to-monitor-public-sector-attendance
- Elpis Groups (2024) Biometric Control of Students in Ivory Coast Schools. https://www.elpisgroups.com/en/education/1111-biometric-control-of-students-in-ivory-coast-schools
- King Cyrus Online (2024) Biometric Devices for Teacher Attendance: GES’s New Initiative Resurfaces. https://kingcyrusonline.com/biometric-devices-for-teacher-attendance-gess-new-initiative-resurfaces/
- Data Rosetta Stone (2023) Understanding the Differences between UML and Merise. https://data-rosetta-stone.com/
- Larman, C. (2024) Applying UML and Patterns: An Introduction to Object-Oriented Analysis and Design and Iterative Development. 3rd Edition, Pearson, 627.
- Zhao, W., Chellappa, R., Phillips, P.J. and Rosenfeld, A. (2003) Face Recognition: A Literature Survey. ACM Computing Surveys ( CSUR ), 35, 399-458. https://doi.org/10.1145/954339.954342
- Introna, L.D. and Nissenbaum, H. (2009) Facial Recognition Technology: A Survey of Policy and Implementation Issues. Center for Catastrophe Preparedness and Response, New York University, 41. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1437730
- Jain, A.K., Ross, A. and Prabhakar, S. (2004) An Introduction to Biometric Recognition. IEEE Transactions on Circuits and Systems for Video Technology , 14, 4-20. https://doi.org/10.1109/TCSVT.2003.818349
- Kavanagh, M.J. and Johnson, R.D. (2017) Human Resource Information Systems: Basics, Applications, and Future Directions. 4th Edition, SAGE Publications, 592.
- NITI Aayog (2020) Leveraging AI for Transforming Education in India. Government of India. https://www.niti.gov.in
- Norazah, M.N., Zaidatun, T. and Zaharah, H. (2019) The Use of Biometric Attendance Systems in Malaysian Schools: Impact and Challenges. International Journal of Educational Management , 33, 963-976.