YOLO-Powered Smart Entry System with ID Card and Face Recognition

Abstract

Identification cards play a crucial role in verifying individuals’ affiliation with an organization, ensuring security, and streamlining attendance management. This project aims to develop an automated system for detecting and recognizing ID cards using deep learning and computer vision techniques. By leveraging Convolutional Neural Networks (CNN), OpenCV, and object detection algorithms such as YOLO and LBPH, the system accurately determines whether a person is wearing their designated ID card. If an individual is identified without an ID card, the system flags a violation, captures their image, and notifies the administrator via e-mail for verification. Additionally, the system automates attendance tracking by matching facial features with registered ID card data. This solution enhances security in educational institutions, workplaces, and other organizations by restricting unauthorized access and improving operational efficiency.