Real-Time Face Mask Detection in Video Streams Using Deep Learning Technique

Face masks are necessary during the worldwide pandemic to prevent the transmission of infectious diseases. This research proposes a deep learning-based system for detecting face masks in live video feeds in real-time. The system's goal is to automatically determine if people on a video broadcast are hiding their faces behind masks. To do this, a deep convolutional neural network architecture is sued and is trained on a huge dataset of annotated photos that include both masked and unmasked faces. The existing systems struggle to handle large-scale deployment and real-time processing of video streams for face mask detection. The network was built to learn features that reliably identify masks or no masks on faces. The transfer learning method is employed for fine-tuning in order to enhance the network's ability to generalize. Further, this study employs a powerful detection pipeline that uses hardware acceleration and parallel processing approaches to deal with the real-time nature of video streams. Since the channel processes video frames in real-time, it can be used in places where fast detection is critical, such as hospitals, airports, and other public buildings. To measure the proposed system's efficacy, large trials are run over various video datasets, each with its unique combination of circumstances and camera angles. The findings show that the proposed Convolutional neural network method is effective, comparing with Dataset-1 and Dataset-2 with real-time processing speed and good accuracy. Solution is accurate 95% to 99% and efficient than the current techniques.