Efficiently tracking and recognition of human faces in real-time video stream with high accuracy and performance

Real time tracking and recognition of human faces in video streams is a critical challenge in computer vision. Existing systems often struggle to balance accuracy and performance, particularly in dynamic environments with varying lighting conditions, occlusions, and rapid movements. High computational overhead and latency further hinder their deployment in realworld applications. These limitations underscore the need for a robust solution capable of maintaining high accuracy and real-time efficiency under diverse conditions. This research addresses these challenges by developing a deep learning-based system that efficiently tracks and recognizes human faces in real-time video streams. Proposed system integrates advanced face detection models you only look once version 5 (YOLOv5) with state-of-theart tracking algorithms, such as deep simple online and real time tracking (SORT), to ensure consistency and robustness. By leveraging graphics processing unit (GPU) acceleration, the system achieves optimal performance while minimizing latency. Multi-frame analysis techniques are incorporated to enhance accuracy in detecting and recognizing faces, even under challenging conditions such as partial occlusions and motion blur. Developed system has broad applications across multiple domains, including surveillance and security, where it can enhance real-time monitoring in crowded environments for seamless face tracking in interactive systems. By focusing on efficiency, robustness, and adaptability this work offering a scalable and high-performance solution for real-time human face tracking and recognition.