AI Powered Face Recognition And Attendance System

Abstract

Artificial Intelligence (AI) has emerged as a key driver of innovation in automation, data analysis, and intelligent decision-making. One of its most impactful applications is face recognition, which enables reliable and contactless identity verification through advanced computer vision and deep learning techniques. Face recognition systems analyze distinctive facial attributes to accurately identify individuals, making them suitable for applications such as security monitoring, access control, healthcare systems, and institutional management. This project presents an AI-powered face recognition and attendance system designed to automate attendance recording using real-time video input. The system employs convolutional neural networks (CNNs) along with Python-based libraries such as OpenCV, Dlib, and DeepFace to achieve accurate and efficient facial recognition. By integrating software intelligence with low-cost hardware components including a Raspberry Pi, webcam, and LCD display, the proposed solution offers a compact, economical, and dependable attendance management system with minimal human intervention. The automated approach significantly reduces time consumption, minimizes manual errors, and enhances security. In addition to technical benefits, the system supports digital transformation by promoting contactless and hygienic operations, which are increasingly important in modern institutional environments. Owing to its modular and scalable design, the system can be extended to applications such as smart surveillance, access control, and workforce monitoring. Overall, the project demonstrates the practical effectiveness of AI-driven face recognition in developing secure, intelligent, and efficient automated solutions