Face Recognition based Automated Smart Attendance using Hybrid Machine Learning Algorithms and Computer Vision

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. Deep Learning (DL)-based face detection, when combined with Principal Component Analysis (PCA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) classification enhances system performance and accuracy. Enrollment generates unique identifiers, and regular updates to a centric dataset in order to make attendance tracking easier. This research promises to manage attendance in educational institutions in an efficient and accurate manner. The results show how effective automated facial recognition systems can be, since the approach reaches 96.8% accuracy.