A Cost-Effective Real-Time Attendance Solution Using Raspberry Pi and Node-RED

Abstract

Introduction: The objective of this research is to develop an economical, real-time attendance system based on face detection on a Raspberry Pi, driven by Edge AI and IoT technologies for rapid and secure processing. To make the system accessible even to non-programmers, it includes easy-to-use tools such as Node-RED for simple visual programming. With in-device image processing, hybrid storage (local and cloud), and auto-alerts, the system provides a secure, scalable, and dependable solution perfect for smart campuses, new-age workplaces, and industrial settings.

Methods: To test different connectivity and automation requirements, the system is tested in four different configurations:  Case 1 is entirely offline with a Raspberry Pi and local database to identify faces. Case 2 incorporates real-time cloud access through the inclusion of Firebase. Case 3 adds Node-RED to the local environment to provide automation options such as dashboards and email notifications. Case 4 uses MariaDB, Node-RED, and the Pi Camera for an entirely automated, scalable application with real-time notification—perfect for institutional or enterprise applications.

Results: All four implementation scenarios validate that the real-time face recognition-based attendance system operates efficiently and with minimal latency. With edge processing using Raspberry Pi, it minimizes reliance on external servers. Node-RED streamlines workflow automation, allowing seamless attendance tracking, real-time monitoring, and instant email notifications through a simple-to-use dashboard. With support for both on-premises (MariaDB) and cloud (Firebase) storage, the system provides scalable deployment options on a per-scalability and connectivity basis. Generally, it provides a secure, efficient alternative to legacy biometric and RFID-based systems and is appropriate for various environments.

Conclusions: The method provides a scalable, automatic, and cost-effective face recognition attendance system. The solution perfectly integrates hardware, software, and cloud services to deliver a real-time, smart attendance tracking system with Raspberry Pi and Node-RED.