Fast and Efficient Real-Time Facial Recognition System Using Raspberry Pi and IoT

Recent advancements in technologies such as artificial intelligence, computer vision, and the Internet of Things have expanded into numerous fields, particularly enhancing surveillance systems that demand real-time facial recognition for security. This study introduces a novel integration of edge computing and computer vision to create an efficient, cost-effective facial recognition system with real-time capabilities. Unlike traditional systems that rely on high-powered servers or cloud infrastructure, which often result in latency, privacy concerns, and increased costs, this approach utilizes Convolutional Neural Networks (CNNs) and the You Only Look Once (YOLO) algorithm on edge devices like the Raspberry Pi. By combining CNNs for precise image classification with YOLO for fast object detection, the system performs high-speed processing directly on the edge device, enabling secure and efficient facial recognition without the need for continuous cloud connectivity. This research thoroughly examines both hardware and software optimizations, showing how this edge-based solution not only reduces latency but also provides scalability and flexibility, making it well-suited for practical security applications.