Automated ID Card Detection and Penalty System Using YOLOv5 and Face Recognition

Abstract

In institutions such as universities, corporate offices, and restricted-access areas, enforcing ID card compliance is critical for ensuring security, tracking attendance, and maintaining discipline. Manual enforcement is often inefficient and prone to oversight. To address this, we propose an automated ID Card Detection and Penalty Mechanism that leverages deep learning models for object detection and facial recognition. The system utilizes YOLOv5 for real-time identification of ID cards worn by individuals in front of a camera. If the system fails to detect an ID card, it automatically initiates a secondary process that uses facial recognition to identify the person, predicts their roll number, and triggers an alert mechanism. This includes sending an automated email notification to a predefined recipient, reporting the incident along with the identified individual's details.The system is trained specifically on a dataset comprising known faces and ID card positions to ensure high accuracy in controlled environments. It includes a user-friendly interface where users can start the camera, initiate detection, and send email notifications directly through the GUI. The model is effective in both detecting the presence of ID cards and in handling non-compliance scenarios by linking the individual’s identity to the infraction. Experimental evaluations show that the system performs reliably across various lighting conditions and backgrounds, with minimal false detections. The proposed solution offers a scalable and efficient method to automate ID enforcement, enhance security monitoring, and reduce dependency on manual supervision.