IDENTIFICATION OF PEOPLE FOR REGISTRATION AND CONTROL VIA ARTIFICIAL INTELLIGENCE [IPRC-AI]

Abstract

This thesis describes the development of a facial recognition system that uses machine learning, cloud computing, and image processing to optimize classroom calls. To ensure accuracy, the system first processes the captured photos using a facial recognition model trained to identify the students. The application works on a web interface using Node.js on the backend, Docker for encapsulation, and Kubernetes for resource management on Google Cloud Platform. Furthermore, the system complies with privacy and data security regulations, such as encryption and adherence to the GDPR. The solution was tested to ensure its effectiveness and usability.

Keywords: recognition; accuracy; optimize; regulations; effectiveness.