# Contactless Real-Time Eye Gaze-Mapping System Based on Simple Siamese Networks
## 1 Introduction  
The human eye is a primary source of information in the field of human-computer interaction (HCI), providing brightness, motion, and depth information. As such, understanding the human visual system is crucial to visual HCI research [1]. The unique physical characteristics and movement patterns of the human eye allow us to determine an individual's focus-of-attention and emotional state. By analyzing essential visual perception and cognitive organ data, we can collect core information on human behavior [2]. Research on gaze recognition has rapidly advanced since the 1970s [3]. Gaze recognition technology has been studied primarily to assist people with disabilities, leading to the development of specialized eye-tracking devices [4]. Gaze recognition has been subdivided into various fields, such as gaze-based user interfaces and human-cognition research [5; 6; 7], and has also been introduced to prevent driver drowsiness with advanced automobile driving-assistance systems (ADAS) [8]. With the growth of wearable electronic devices and computing performance, we are now able to analyze eye movements in greater detail. Existing eye-tracking and recognition methods often require participants to wear glasses-like electronic devices [9], in which an eye camera has been installed in the device to detect the field of vision for each eye.The final gaze is determined by the location of the pupil region within both of the detected eye regions. The pattern of the human eye and gaze can reveal an individual's needs, intentions, and state of mind in the process of understanding social interactions [1]. However, most eye recognition studies to date have been performed using wearable glasses-like electronic devices [8]. These experiments have required participants to be aware of the gaze recognition test environment, which thus affected their behavior due to the hardware devices they had to wear. To reliably extract various reaction variables from the experimental test units and create a natural experimental environment that eliminated participants' psychological factors, a non-contact method was required. This method needed to record the pattern of the natural gaze, eliminating the psychological factors that contributed to human error. In this work, we proposed a non-contact gaze-mapping method that could recognize gaze patterns without the use of a glass-like device and that could map a user's gaze regardless of their location or height.However, gaze-recognition performance in an unconstrained environment without a wearable device would be affected by external factors, such as measurement distance, lighting, and whether the pupil region had been captured, as shown in Figure 1. The coarse gaze area was acquired according to the head pose, and the fine final gaze area was mapped through a gaze-recognition module. The method proposed in this work intuitively comprehended the user's gaze via a non-contact method, that is, without using a glasses-like device, and it mapped the eye gaze regardless of the user's location. The proposed method consisted of a three-step process. (1) The face module was executed (detection, alignment, and recognition). (2) The eye region was extracted from the face region, and the gaze was estimated. (3) The final gaze was mapped to the target of the gaze panel by integrating the depth information obtained by the depth camera, gaze recognition information, and head pose. We implemented the entire gaze mapping process through the parallelization and optimization of the system to operate on the edge device, NVIDIA TX2, at 7.5 fps. This paper's main contributions were the following: We created the GIST gaze-mapping (GGM) dataset for training the gaze-matching network. The GGM reflected the various user environments in which the dataset could be generalized, including a user's height, distance change, and profile gaze-posture. The depth information obtained through the depth sensor camera automated the parameter optimization, ensuring that tuning would not be required during inference.