DESIGN OF A FACE RECOGNITION TECHNIQUE BASED MTCNN AND HEAD POSE ESTIMATION

Abstract

Artificial Intelligence and IoT have always attracted a lot of attention from
scholars and researchers, not only because of their high applicability but also
typical technologies of the Fourth Industrial Revolution. The hallmark of AI is its
self-learning ability, which enables computers to predict and analyze complex
data such as fingerprints, irises, and faces. The study proposes a solution for a
mechatronic system that integrates AI's face recognition capabilities to create an
attendance system and assess student attendance. The model's accuracy ranges
from 90% to 95%. The study's results are compared with recent research
demonstrating the system's usability. Therefore, the authors apply the training
process's outcomes to construct an attendance and diligence assessment system
that recognizes students' faces.