A FACE DETECTION AND RECOGNITION-BASED ONLINE ATTENDANCE SYSTEM USING COMPUTER VISION

Abstract

Face recognition has drawn a lot of attention recently and is a crucial issue in many applications, including access control, security systems, and credit card verification and identification of criminals. This study suggests three primary subsystems, including autonomous door access control, face detection, and face recognition. By adapting the principal component analysis (PCA) approach to the fast based principal component analysis (FBPCA) approach, the face identification and detection process is achieved. The captured image is recognized using a web camera and compared with the image in the database. To achieve the goal of identification, image processing and recognition are applied to the actual image modification and transformation. This project focuses on the design and of a facial recognition attendance system using computer vision technique. The system aims to automate the process of identifying and verifying individuals in an organization based on their facial features. By leveraging advanced algorithms, image processing techniques, and deep learning models, the system achieves accurate and real time facial recognition. The project involves data collection, pre-processing, feature extraction, and system integration to develop a comprehensive facial recognition solution