# Contactless Real-Time Eye Gaze-Mapping System Based on Simple Siamese Networks
## 5 Conclusions  
Gaze mapping is a method used to measure the movement of the eyeball in order to determine the location at which a person is looking and how long they fixate on a certain point. Since our eyes are one of the primary organs used for decision-making and learning, accurate measurements and the understanding of visual attention are essential. This study aimed to construct an experimental environment for an eye-tracking simulation configuration used in neuromarketing to measure the distance at which human unconscious emotional information operates (i.e., the distance at which simple exposure effects occur). The resulting gaze-mapping (GGM) dataset provided various possibilities for customization and training for generalized performance, including a person's height, distance changes, and profiles of head poses. In contrast to previous studies, the proposed non-contact gaze-mapping system could map the user's eye gaze without the need for wearable hardware devices. With real-time recognition and the mapping of the natural gaze without the user's awareness, the system has numerous potential applications for researchers studying human behavior as well as in neuromarketing and retail companies. In addition, if the mapping system was used when watching a television program, it could measure the effectiveness of advertising on viewers or apply it to training programs for athletes and pilots. It could be installed in a car and applied as a program that analyzes driver driving patterns and detects drowsiness. However, future research is necessary to extend the distance range for gaze mapping beyond the current implementation, which was limited to the distance in a retail shelving environment. Eye tracking accuracy could be improved by using a large gaze-estimation model, as the real-time processing via edge devices was difficult.  
In our method, accurate measurements were difficult at 40 cm above and below the camera height, and accurate gaze-mapping was difficult when the gaze vector could not be resolved due to a user's eyes being obfuscated or when a user wore glasses. Furthermore, by enabling communication between installed Jetson modules in different commercial section, a large-scale gaze-mapping system could be developed. Finally, extending the facial  
Figure 13: Diagram of gaze-mapping system parallelization. Based on queued and multi-processing, resource use was maximized to improve speed, and CPU resources could be dynamically allocated according to the load of each task in order to improve overall inference speed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hardware** & **Jetson AGX Xavier** & **Jetson TX2** \\ \hline CPU(ARM) & 8-core Carmel ARM CPU @ 2.26 GHz & 6-core Denver and A57 @ 2 GHz \\ \hline GPU & 512 Core Volta @ 1.37 GHz & 256 Core Pascal @ 1.3 GHz \\ \hline Memory & 16 GB 256-bit LPDDR4x @ 2133 MHz & 8 GB 128-bit LPDDR4 \\ \hline Speed & 10 fps & 7.5 fps \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of speculation and performance (fps) on Jetson TX2 and Jetson AGX Xavier.  
recognition module with a facial ID re-identification function could enhance the mapping system's capabilities by identifying the user's trajectory in each commercial section.  
Methodology, H.A.; Software, H.A.; Validation, J.G.; Formal analysis, H.A., J.G. and M.J.; Investigation, J.J., D.K. and M.J.; Data curation, J.J. and D.K.; Writing--original draft, H.A., J.G. and M.J.; Writing--review & editing, J.G. and M.J.; Supervision, M.J.; Project administration, M.J.; Funding acquisition, M.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.