  Human-Computer Interaction(HCI) is a field that studies interactions between
human users and computer systems. With the development of HCI, individuals or
groups of people can use various digital technologies to achieve the optimal
user experience. Human visual attention and visual intelligence are related to
cognitive science, psychology, and marketing informatics, and are used in
various applications of HCI. Gaze recognition is closely related to the HCI
field because it is meaningful in that it can enhance understanding of basic
human behavior. We can obtain reliable visual attention by the Gaze Matching
method that finds the area the user is staring at. In the previous methods, the
user wears a glasses-type device which in the form of glasses equipped with a
gaze tracking function and performs gaze tracking within a limited monitor
area. Also, the gaze estimation within a limited range is performed while the
user's posture is fixed. We overcome the physical limitations of the previous
method in this paper and propose a non-contact gaze mapping system applicable
in real-world environments. In addition, we introduce the GIST Gaze Mapping
(GGM) dataset, a Gaze mapping dataset created to learn and evaluate gaze
mapping.
