ABSTRACT
Monitoring students in the classroom plays a key role in the academic
follow-up. In addition to attendance control, it also allows
studying the impact of absences on learning. In this context, this
paper presents the development of a school attendance management
system using computer vision techniques and convolutional
neural networks. A camera captures images from the classroom.
The system then detects the faces of the people in the image, and
recognizes students using pre-trained convolutional neural network
models. The frequency data generated is stored in the system
database and accessed through a web page. In addition, a mobile
app has been developed to notify student attendance. The initial
results show that the system identifies the registered students and
notifies the mobile device of responsible.