Abstract: In educational institutions, attendance monitoring is a crucial task that ensures students' regular participation in academic activities. Traditional manual methods are time-consuming and prone to errors, while biometric methods such as fingerprint scanning often cause delays due to long queues. This paper presents an efficient attendance management system using face recognition technology. The system captures student images through a camera, detects and recognizes faces using advanced machine learning techniques, and automatically records attendance. The methodology includes image preprocessing, feature extraction, and classification using the Principal Component Analysis (PCA) and Eigenface approach. Experimental results demonstrate high accuracy and efficiency compared to conventional methods.
Keywords: Principle Component Analysis, Convolutional Neural Networks, Facial Recognition, Image Acquisation, Feature Extraction.
|
DOI:
10.17148/IJARCCE.2025.14483
