Cloud-Driven Face Recognition: The Future of Smart Surveillance

This paper proposes a cloud-based facial recognition platform aimed at satisfying the increasing requirement for intelligent surveillance in cities. The main purpose is to support real-time and scalable identity confirmation through live CCTV streams. Lightweight AI models like Ultralight and YOLO Face are used for low-latency face detection, while classification algorithms such as K-Nearest Neighbors (KNN) or Convolutional Neural Networks (CNN) are used for accurate identification of persons. The approach utilizes cloud deployment and multithreading to execute video frames in parallel, which drastically minimizes latency and enhances responsiveness. The major findings prove the system’s capability to manage real-time monitoring in multiple locations with high computational effectiveness. Through the use of cloud infrastructure, the solution bypasses local hardware constraints and provides a flexible, scalable, and robust platform for the purpose of improving public safety and automated city surveillance.