## 2 Related Work  
Implementing high-performance secure streaming system with face masking for remote education has been investigated thoroughly in the medical filed [15]. Table 1 shows a comparison of related work for the face masking. We consider the following three challenges in implementing consecutive and effective face masking: (1) It is difficult to achieve highly accurate face detection with limited computational resources and cost. (2) Technical barriers exist in terms of correlation distance and viewing angle. (3) Large communication overhead to send raw data to the cloud in real time, making it difficult to protect security. Pervasive secure streaming system with face masking is inseparable from consecutive processing, even during the early and late stages. [PERSON] et al. [16] propose a framework that adds the loss of service quality to the loss function to ensure the generation of de-identified face images with guided quality preservation for the face privacy protection. [PERSON] et al. [17] propose an image modification mechanism that inserts virtual face images that strictly match a given similarity and uses blockchain as a storage area. There is also a case [18] that studies a facial masking scheme for video surveillance systems to protect individual privacy from videos captured by CCTV cameras. [PERSON] et al. [19] present a cloud-based approach to securely recognize human activities. They secure only four images, including one motion history image and three depth motion maps, which are highly saving the data overhead. Unlike these studies, our approach differs from them. We perform face mosaicing in the video stream in real-time, and the ultimate goal is to facilitate medical education by distributing and viewing the video through a edge computing with Web browser. Face detection must first be performed to achieve facial privacy protection in video distribution [15]. On the one hand, we utilize image-based bone sensing techniques to overcome this limitation, which we believe can more smoothly extract features only from face parts. A typical bone sensing technique is the PoseNet model [20]. It is a trained bone sensing model that uses TensorFlow.js, enabling bone sensing in an offline edge environment. Specifically, input any single 2D image and the XY coordinates of 17 body parts and automatically output recognition accuracy values. By developing and driving the PoseNet program, it can be programmed using JavaScript source code in a Web browser or independent Node.js source code. On the other hand, Web Real-Time Communication (WebRTC) [21] technology can be used to realize video distribution using a Web browser. PeerJS technology is a WebRTC communication server that uses JavaScript libraries. PeerJS technology allows webcam video and audio data to be received between browsers (peer-to-peer). On the other hand, WebSocket technology is a WebRTC communication standard for bidirectional communication between a Web browser and a server. We use PeerJS technology for video distribution and WebSocket for bidirectional text message communication. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Research Authors** & **Masking Target** & **Materials and Methods** & **Intrusiveness** & **Contributions** \\ \hline [PERSON] et al. [18] (2018) & Face & CCTV cameras, video surveillance, face identification & Exist & Found optimal masking values in case study \\ [PERSON] et al. [17] (2020) & Face & Closed-circuit television, CCTV cameras, video surveillance & Exist & Inserted a virtual face used a blockchain as the storage area \\ [PERSON] et al. [19] (2020) & Face & RGB-D sensors, deep CNN, device & None & Improved the security-recognition accuracy \\ [PERSON] et al. [15] (2020) & Face, body key points & De-identification, keypoint preservation, face-swapping & Exist & Though video processing to \\ Our research in this paper (2022) & Face, eye line & General computer, bone sensing, edge computing & Exist & de-identify subjects \\ \hline \hline \end{tabular}
\end{table}