## 1 Introduction
### Related Works  
In recent years, the progress of deep-learning technologies has become very important, especially in the field of computer vision and pattern recognition. Through deep-learning technology, inference models can be trained to realize real-time object detection and recognition. For the deep-learning-based designs in [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37], the eye tracking systems could detect the location of eyes or pupils, regardless of using the visible-light or near-infrared image sensors. Table 1 describes the overview of the deep-learning-based gaze tracking designs. From the viewpoints of device setup, the deep-learning-based gaze tracking designs in [22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37] can be divided into two different styles, which include the non-wearable and the wearable types. For the deep-learning-based non-wearable eye tracking designs, in [22], the authors proposed a deep-learning-based gaze estimation scheme to estimate the gaze direction from a single face image. The proposed gaze estimation design was based on applying multiple convolutional neural networks (CNN) to learn the model for gaze estimation from the eye images. The proposed design provided accurate gaze estimation for users with different head poses by including the head pose information into the gaze estimation framework. Compared with the previous methods, the developed gaze estimation design increased the accuracy of appearance-based gaze estimation under head pose variations. In [23], the purpose of this work was to use CNNs to classify eye tracking data. Firstly, a CNN was used to classify two different web interfaces for browsing news data. Secondly, a CNN was utilized to classify the nationalities of users. In addition, the data-preprocessing and feature-engineering techniques were applied. In [24], for emerging consumer electronic products, an accurate and efficient eye gaze estimation design was important, and  
Figure 1: Visible-light pupil tracking results by the previous ellipse fitting-based method. the products were required to remain reliable under difficult environments with low-power consumption and low hardware cost. In this paper, a new hardware-friendly CNN model with minimal computational requirements was developed and assessed for efficient appearance-based gaze estimation. The proposed CNN model was tested and compared against existing appearance-based CNN approaches, achieving better eye gaze accuracy with large reductions of computational complexities. In [25], iRiter technology was developed, which can assist paralyzed people to write on screen by using only their eye movements. iRiter detects precisely the movement of the eye pupil by referring to the reflections of external near-infrared (IR) signals. Then, the deep multilayer perceptron model was used for the calibration process. By the position vector of pupil area, the coordinates of the pupil were mapped to the given gaze position on the screen. In [26], the authors proposed a gaze zone estimation design by using deep-learning technology. Compared with traditional designs, the developed method did not need the procedure of calibration. In the proposed method, a Kinect was used to capture the video of a computer user, a Haar cascade classifier was applied to detect the face and eye regions, and data on the eye region was used to estimate the gaze zones on the monitor by the trained CNN. The proposed calibration-free method performed a high accuracy to be applied for human-computer interaction. In [27], which focused on healthcare applications, the high accuracy in semantic segmentation of medical images were important; a key issue for training the CNNs was obtaining large-scale and precisely annotated images, and the authors sought to address the lack of annotated data used with eye tracking method.