Learnify: securing online learning through facial recognition

The Learnify system solves vital attendance management difficulties in e-learning through its development of a platform that integrates facial recognition features. The benefits that e-learning platforms provide students with educational access beyond traditional classrooms are hindered by their inability to create reliable attendance tracking which causes students to cheat and places extra workload on administrative staff. Learnify implements automation for attendance tracking through the combination of Python (Flask) and OpenCV with MySQL to address these problems. The problem identification phase established that manual and semi-automated attendance systems lacked accuracy and data integrity thus requiring replacement. The development of the system under the Waterfall methodology followed a sequential process starting from planning through requirements analysis into system architecture design then implementation and verification stages. Lab tests demonstrated that the system achieved 60% recognition success through methods including facial data capture using OpenCV and processing with a CNN-based recognition model, while confidence scores were compared against established thresholds. Users gave positive feedback about usability through System Usability Scale (SUS) ratings. The system modules for attendance tracking along with reporting and user management achieved valid results that satisfied user demands and research criteria. Learnify creates better interaction and fairness in online learning by implementing automated attendance tracking systems that provide immediate record security. The two-semester period of the project led to Learnify becoming a flexible solution for school attendance management that demonstrates opportunities for additional development in upcoming software versions.