## 4 Preliminary Experiment
### Purpose and Experimental Setup  
The purpose of this section is to compare the effectiveness of the methods proposed in Section 3 from various perspectives. Specific experimental settings are shown in Table 3. We use the proposed method to condition different viewing angles and interpersonal distances of human faces photographed by a depth camera (see Figures 9 and 10). Based on this, we are trying to figure out to what size the proposed method can mask the face for each successive image. Moreover, we do not evaluate only the proposed method but also collect the results of experiments with the same approach as the proposed method using features output from recent face recognition technology (see Figure 11). We compare and discuss bone sensing and face recognition results. In this experiment, all subjects provided consent to the processes. Our goal is to record 140 frames (i.e., roughly 5 s) in each of five different head directions (i.e., front, top, down, left, right) at three different distances (i.e., near, middle, far) from the person to the camera. We used a depth camera to record integrated movies (i.e., 140 \(\times\) 5 = 700 frames) of five different head orientations at three different subject positions at three different distances and the distance data measured for each. The video recording and distance data measurement were realized using Python programming on Jupyter Notebook [22] with a general-purpose computer and introducing two libraries: OpenCV [23] and Pyrealsense2 [24].  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Item** & **Technologies and Libraries Used** \\ \hline HTTP server & express, http \\ WebSocket technology & socket.io \\ PeerJS technology & peer \\ Random ID generation & uuid \\ Basic authentication & basic-auth-connect \\ Bone sensing & PoseNet Model \\ Web browser & Chrome (101.0.1951.54) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technologies used in the implementation of the proposed system.  
Figure 9: Experimental tools and settings: (**a**) Intel RealSense Depth Camera D435. (**b**) Experimental settings with three distances.  
Figure 11: Outputting feature positions from bone sensing and face recognition, respectively.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Items** & **Contents** \\ \hline Target space & One seat in ES4 Nakamura lab \\ Subject information & One person (male, 20 s) \\ Video total length & 700 frame \\ Video resolution & 640 \(\times\) 480 \\ Frame time interval & 30 fps \\ Shooting method & Intel RealSense Depth Camera D435 [25] \\ Number of head directions & 5 \\ Head directions & Front, up, down, left, right \\ Video length of each head direction & 140 frames \\ Number of distances from person to camera & 3 \\ Distances from person to camera & About 50 cm (near), 100 cm (middle), 150 cm (far) \\ Used technologies & Bone sensing (posenet.js), face recognition (face-api.js) \\ Number of masking face methods & 2 \\ Masking face methods & Eye line mosaic, face mosaic \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental settings.  
Figure 10: Representative eye line mosaic effects using bone sensing and depth camera.