Students’ Attendance Management in Higher Institutions Using Azure Cognitive Service and Opencv Face Detection & Recognition Attendance System

This research aimed at studying the current methods of attendance used at higher institutions of learning in Uganda and the feasibility of using facial biometrics as a new method of capturing attendance. Facial biometrics is distinct from other biometrics because it can be carried out without the consent of the person involved. As a result, the researcher developed a face recognition attendance system using OpenCV and Microsoft Azure CS. Questionnaires, interviews, and observations were used to capture data for the research. The data were analyzed using SPSS to get the requirements and systems functionalities. Object-Oriented Design tools were used to model the architecture of the system. Data Flow Diagram, Use-Case Diagram, Activity Diagram, and Flow Chart were used for processing whereas Entity Relation Diagram was used for data modeling. The system was designed to facilitate attendance management of a large number of attendees with ease. Efficiency and reliability were essential features of the system. Data visualization was provided to help management make informed and timely decisions on management matters that are related to attendance. The system was developed using python Tkinter, OpenCV, and Azure CS as mentioned above. The data (images) used by the system were stored in the cloud for accessibility by multiple users. The system was tested thoroughly using various testing types to uncover and fix errors and to minimize the severity of failures.