A Data Science-Driven Face Recognition Attendance System Integrated with a Voice Assistant

Accurate and efficient attendance tracking is a critical component of institutional management in educational and organizational environments. Traditional methods such as manual roll calls and biometric systems like fingerprint recognition often suffer from inefficiencies, hygiene concerns, and susceptibility to manipulation. This manuscript proposes a robust, contactless attendance management system based on real-time face recognition, enhanced with voice assistant integration and powered by advanced machine learning techniques. The system leverages convolutional neural networks (CNNs) for facial feature extraction, the Viola-Jones algorithm for face detection, and support vector machines (SVMs) for classification. Additionally, it integrates real-time 3D face tracking and customizable face modeling to enhance recognition accuracy under varying conditions. Attendance data is updated instantly through an IoT-enabled database, ensuring scalability and remote access. Voice assistant capabilities further improve user interaction, offering voice-driven commands and status feedback. Experimental results demonstrate the system’s high accuracy, speed, and reliability, making it a viable solution for modern attendance tracking. This work lays the foundation for intelligent, secure, and user-friendly attendance systems suitable for smart classrooms and automated institutional workflows.