In the era of advanced video surveillance and real-time face recognition applications, the quality of webcam streams plays a pivotal role in accurately identifying faces. The facial webcam streams are of low quality. This research introduces a novel approach for enhancing face recognition in challenging environments by employing real-time super-resolution techniques. The proposed SRGAN+ algorithm can super-resolve webcam streams of faces in real-time, making it suitable for practical applications. Subjecting facial webcam video streams to this innovative super-resolution model, which enhances image quality, finer facial details, and heightened recognition precision, offers a promising solution for bolstering the reliability of face recognition systems. Faces of known individuals are identified and labeled, while encounters with unknown persons trigger real-time alerts and notifications. This research demonstrates the practicality of super-resolution in home-premise webcam streams and underlines its critical role in improving the accuracy and precision of face recognition in challenging environments.