This study presents the development of a Convolutional Neural Network (CNN)-based guardian facial recognition system designed to enhance safety and attendance management in daycare centers, specifically at Orchid Child Development Center in Science City of Muñoz. Traditional attendance systems that rely on manual logbooks are often inefficient, prone to human error, and lack real-time verification of authorized guardians. The proposed system utilizes a CNN model to accurately classify and verify registered guardians through facial recognition. A webcam captures the guardian’s image during drop-off and pick-up, which is then processed and compared with stored facial data. Once verified, the system automatically records attendance in a CSV-based database and provides real-time monitoring through an admin dashboard. The system improves security, efficiency, and accuracy by ensuring that only authorized guardians can access children.
Cristina C. Alberto (Mon,) studied this question.