Real Time Deep Learning based Face Recognition System Using Raspberry PI

Face recognition technology has gained substantial attention due to its diverse applications in security, authentication, and human-computer interaction. In this study, we demonstrate a practical implementation of face recognition using the Raspberry Pi platform with the integration of deep learning. This research delves into the synergy between the Raspberry Pi with camera module, a cost-effective and portable computing platform, and deep learning techniques, aiming to create an efficient and accessible face recognition system in real time. Our approach involves the development of a tailored Convolutional Neural Network (CNN) model, fine-tuned to operate seamlessly on the Raspberry Pi’s resource-constrained architecture.Through rigorous testing, we demonstrate the real-world applicability of our system, achieving high accuracy in face recognition across various scenarios, including challenging lighting conditions, diverse poses, with or without glasses and distance. Furthermore, our research addresses the practical aspects of power consumption and memory utilization, ensuring the suitability of this solution for edge computing environments. This work underscores the potential of Raspberry Pi and deep learning in democratizing face recognition technology, making it accessible for applications such as access control, surveillance, and interactive systems.