###### Abstract  
Unlike masking human faces from images, facial masking in real-time, frame by frame from a video stream, presents technical challenges related to various factors such as camera-to-human distance, head direction, and mosaic schemes. In many existing studies, expensive equipment and huge computational resources are strongly required, and it is not easy to effectively realize real-time facial masking with a simpler approach. This study aims to develop a secure streaming system to support remote medicine education and to quantitatively evaluate consecutive and effective facial masking using image-based bone sensing. Our key idea is to use the facial feature of bone sensing instead of general face recognition techniques to perform facial masking from the video stream. We use a general-purpose computer and a USB fixed-point camera to implement the eye line mosaic and face mosaic. We quantitatively evaluate the results of facial masking at different distances and human head orientations using bone sensing technology and a depth camera. we compare the results of a similar approach for face recognition with those of bone sensing. As the main results, consecutive face masking using bone sensing is unaffected by distance and head orientation, and the variation width of the mosaic area is stable within around 30% of the target area. However, about three-fourths of the results using conventional face recognition were unable to mask their faces consecutively. facial masking; image; bone sensing; video stream; real-time processing; eye line mosaic; face mosaic; face recognition +
Footnote †: journal: Medical Image and Image Processing  
1 Center of Mathematical and Data Sciences, Kobe University, 1-1 Rokkodai-cho, Nada, Kobe 657-8501, Japan 12 Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan 2  
M.N. In some cases, recognition accuracy is reduced when a face exceeds a certain distance from the camera or when a face is not in front of the camera, thus making it impossible to protect privacy [5]. We also consider the security protection of communication for video distribution in face protection a technical challenge. Our interest is to research and develop secure streaming systems in clinical settings for medical education. For this, we consider the following two technical challenges in this research: (1) Face detection in images and videos has a face size detection limit due to the correlation distance between human and camera [6]. (2) The streaming of video with face mosaics also requires communication security protection [7]. The purpose of this study is to develop a secure streaming system to support remote medicine education and to quantitatively evaluate consecutive and effective facial masking using image-based bone sensing. Our key idea is to integrate Web Real-Time Commutation (WebRTC) [8] and image-based bone sensing with the PoseNet model [9]. As an approach, we first use a USB camera and a general-purpose computer to display a video stream of a clinical site in real-time using a Web browser. Next, we extract features of the human face in the acquired video from the recognition results of the bone sensing PoseNet model. Then, the masking process is applied to the extracted facial features, and \"eye line mosaic\" and \"face mosaic\" algorithms are designed. Furthermore, the video stream with mosaic depicted in HTML Canvas [10] is delivered to the Web browser using PeerJS technology [11]. In this way, it is expected to facilitate the implementation of distance learning in medical education while delivering the mosaicked video in real-time in the classroom. The contribution of the proposed method includes the following three parts: (1) Acquire the video stream in real-time and extract only facial features using bone sensing technology. (2) We design an algorithm to mosaic the eyes and face by utilizing the two-dimensional coordinates of the left and right eyes and ears, respectively.