Year: 2025
Type: article
Abstract: This project presents an automated attendance system that integrates computer vision techniques with a Flaskbased web framework.The system leverages Convolutional Neural Networks (CNN) to perform real-time face recognition for accurately marking student attendance.The primary objective is to eliminate manual processes and improve efficiency in attendance tracking.The design involves capturing facial images through a webcam, training the CNN model with pre-processed facial data, and deploying the trained model on a Flask server for recognition tasks.Upon successful identification, the system automatically records the attendance in a structured database.Experimental results demonstrate a high recognition accuracy and reliability in diverse lighting and background conditions.The study concludes that the proposed system significantly reduces administrative workload and ensures a secure, contactless, and scalable attendance solution for educational institutions.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412928048