Hybrid Face Recognition Model for Student Attendance Tracking Using Machine Learning Algorithms

Attendance tracking in educational institutions is critical, the manual methods are very difficult, in particularly for large numbers of student populations. In this research novelty new methodology proposed work a face recognition based attendance monitoring system that employs deep learning prediction system and computer vision. This system aims to streamline processes, reduce fraud, and improve accuracy. This study has used robust face detection algorithms, combined with Histograms of Oriented Gradients (HOG) feature extraction to yield a comprehensive database of authorized students face detection using Deep Learning (DL), when integrated with Principal Component Analysis (PCA), Support Vector Machine (SVM), KNearest Neighbor (KNN), and CNN classification, improves both system performance and accuracy. Enrollment generates unique identifiers, and regular updates to a centric dataset in order to make attendance tracking easier. This study promises to regulate attendance in schools in a practical and precise way.