A Scalable Real-Time Attendance Tracking System Based on Face Recognition

This paper proposes a real-time attendance tracking system leveraging Apache Kafka and Spark Streaming for data processing and facial recognition, built on a big data framework. While previous research has predominantly focused on algorithms, often neglecting the practicality and scalability of the system, this study emphasizes constructing a practical, scalable solution for real-world applications. To achieve high accuracy, state-of-the-art deep learning algorithms were employed, complemented by a reranking mechanism using the RANSAC algorithm. These algorithms were implemented on Apache Spark, which is a powerful framework for big data processing, to ensure the scalability of the system. Additionally, a web application is developed for attendance and device management, demonstrating the system's applicability and effectiveness in practical scenarios.