Abstract: Attendance management is an essential task in educational and organizational settings, often plagued by inefficiencies in traditional methods such as manual roll calls and sign-in sheets. This paper proposes an AI and ML-based Face Recognition Attendance System that leverages the NVIDIA Jetson Nano for real-time, automated attendance tracking. The system utilizes advanced facial recognition technology, employing the OpenCV Deep Neural Network (DNN) framework and Haar Cascade Classifier for accurate face detection and recognition. The NVIDIA Jetson Nano's GPU-accelerated capabilities ensure efficient processing, enabling the system to operate in real-time without reliance on external servers.
Key components of the system include capturing facial data through a webcam, detecting faces using Haar Cascade, and recognizing them through the OpenCV DNN framework. Attendance data is logged automatically and updated in real-time in an Excel spreadsheet, simplifying reporting and reducing administrative burdens. This fully automated system addresses challenges associated with traditional and biometric attendance methods, including time inefficiency, scalability limitations, and susceptibility to errors.
Experimental results demonstrate a recognition accuracy of 94% under standard conditions and 88% in low-light environments, with an average processing time of 1.5 seconds per recognition. These results highlight the system's reliability, scalability, and adaptability for diverse applications. The proposed solution aligns with the global trend toward digital transformation, showcasing the potential of AI and ML in addressing real-world challenges while maintaining data security and integrity.
Keywords: Face Recognition, Deep Learning, OpenCV DNN, NVIDIA Jetson Nano, Haar Cascade.
Copyright Form