Advancements in Real-Time Face Recognition Algorithms for Enhanced Smart Video Surveillance

Smart video surveillance systems are essential for guaranteeing security in a variety of settings. The capacity to recognize and follow people of interest inside video feeds is made possible by real-time face recognition, which is a key component of these systems. The refinement of a real-time face recognition system designed for video surveillance applications is the focus of this research. We proposed a technique that can effectively recognize faces in a variety of lighting situations, poses, and occlusions by utilizing innovative combination of Haar Cascade and Convolutional Neural Networks. To balance computational difficulties and increase scalability, the merging of edge computing and cloud-based solutions are also investigated. The performance of the proposed system is evaluated by vigilant evaluation, demonstrating its applicability in real-world circumstances. Our research aims to advance video surveillance technology with an emphasis on the synergy of real-time face recognition for more secure and effective surveillance systems.