# Contactless Real-Time Eye Gaze-Mapping System Based on Simple Siamese Networks
## 4 Experiments  
### GGM Dataset  
The GGM dataset was produced by generating the test environment for 9 cases by changing the gaze-area width (0.75 m, 1 m, 1.5 m) and the user's relative position (left, right, center) from the camera, at 0.5m intervals. There were 5 users in the GGM dataset, and they had different heights (170 cm-185 cm). As shown in Figure 7, all images were acquired using the Intel RealSense depth camera D435. The depth resolution and FPS of the camera were 1280 \(\times\) 720 and 30 fps, respectively, and the working range was from 0.11 m up to 10 m. The depth field-of-view was \(85.2\times 58\) and was utilized to calculate the relative coordinates, as shown in Figure 8. The GGM dataset consisted of a pair of depth images and labels. The file name for each record was created based on the user location and user ID of the camera. The label included the start and end frames when the user was gazing at a specific location. In each row, the attributes consisted of facial landmarks (obtained  
Figure 5: Gaze-matching module working structure. The module consisted of a total of three sub-modules. The head-pose estimator extracted the head-pose vector using bounding boxes and landmarks collected from the Jetson TX2.The gaze-matching network extracted the gaze area within the image by combining the head-pose vector, gaze vector, bounding box, and landmarks. The logMgr logged the events that occurred in each module and recorded them in the database.  
Figure 6: PyRO: A library that enabled Python objects on a remote machine by utilizing Python remote objects. Face ID was shared by the DB server in each Jetson TX2.  
using dlib + MTCNN), the gaze vector, and the head-pose estimation values, as illustrated in Figure 9.  
Figure 8: Description of creation of the gaze-matching network dataset. The gaze module output the gaze vector and the image depth information provided by the depth camera, which was then combined into a single vector. The final ground truth was produced based on the user’s gaze area and relative coordinates, according to the camera data.  
Figure 7: The gaze-mapping system hardware configuration. The Intel RealSense depth camera D435, which could acquire depth information, and NVIDIA’s Jetson TX2 module were used.  
The test environment of the gaze-mapping system closely resembled an actual environment of shelving in a large retail store. It was designed to mimic the eye-tracking simulations commonly used in the field of neuromarketing, as depicted in Figure 10. In particular, our gaze-mapping system was tested within a distance of 1 m, in which simple exposure effects would occur, that is, where human unconscious emotional information would be detected in a real-world retail environment.  
The user's appearance in the GGM dataset changed when they wore glasses, hats, and masks. In the dataset, people stared up to 30\({}^{\circ}\) to the left and right. The gaze panel was composed of 6 \(\times\) 6 grids, as shown in Figure 11, and was divided into 36 sections. The size of each grid was 0.17 m \(\times\) 0.23 m, and the size of the entire panel was 0.9 m \(\times\) 1.38 m. The camera was installed at the height of 1.5 m above the ground.  
Figure 10: Description of GGM dataset acquisition method and experimental environment. The user gazed at a specific area (6 \(\times\) 6 grid) in front of the shelves.  
Figure 9: The configuration of the GGM dataset. The depth image acquired by D435 and the label comprised a pair, and the ID and start–end frame times were recorded.