## 1 Introduction
### Related Works
The hypothesis was that segmentation masks generated to help eye tracking would be very similar to those rendered by hand annotation, and the results demonstrated that the eye tracking method created segmentation masks, which were suitable for deep-learning-based semantic segmentation. In [29], for the conditions of unconstrained gaze estimation, most of the gaze datasets were collected under laboratory conditions, and the previous methods were not evaluated across multiple datasets. In this work, the authors studied key challenges, including target gaze range, illumination conditions, and facial appearance variation. The study showed that image resolution and the use of both eyes affected gaze estimation performance. Moreover, the authors proposed the first deep appearance-based gaze estimation method, i.e., GazeNet, to improve the state of the art by 22% for the cross-dataset evaluation. In [30], a subconscious response influenced the estimation of human gaze from factors related to the human mental activity. The previous methodologies only based on the use of gaze statistical modeling and classifiers for assessing images without referring to a user's interests. In this work, the authors introduced a large-scale annotated gaze dataset, which was suitable for training deep-learning models. Then, a novel deep-learning-based method was used to capture gaze patterns for assessing image objects with respect to the user's preferences. The experiments demonstrated that the proposed method performed effectively by considering key factors related to the human gaze behavior. In [31], the authors proposed a two-step training network, which was named the Gaze Estimator, to raise the gaze estimation accuracy on mobile devices. At the first step, an eye landmarks localization network was trained on the 300W-LP dataset, and the second step was to train a gaze estimation model on the GazeCapture dataset. In [28], the network localized the eye precisely on the image and the CNN network was robust when facial expressions and occlusion happened; the inputs of the gaze estimation network were eye images and eye grids. In [32], since many previous methods were based on a single camera, and focused on either the gaze point estimation or gaze direction estimation, the authors proposed an effective multitask method for the gaze point estimation by multi-view cameras. Specifically, the authors analyzed the close relationship between the gaze point estimation and gaze direction estimation, and used a partially shared convolutional neural networks to estimate the gaze direction and gaze point. Moreover, the authors introduced a new multi-view gaze tracking data set consisting of multi-view eye images of different subjects. In [33], the U2 Eyes dataset was introduced, and U2 Eyes was a binocular database of synthesized images regenerating real gaze tracking scripts with 2D/3D labels. The U2 Eyes dataset could be used in machine and deep-learning technologies for eye tracker designs. In [34], for using appearance-based gaze estimation techniques, only using a single camera for the gaze estimation will limit the application field to short distance. To overcome this issue, the authors developed a new long-distance gaze estimation design. In the training phase, the Learning-based Single Camera eye tracker (LSC eye tracker) acquired gaze data by a commercial eye tracker, the face appearance images were captured by a long-distance camera, and deep convolutional neural network models were used to learn the mapping from appearance images to gazes. In the application phase, the LSC eye tracker predicted gazes based on the acquired appearance images by the single camera and the trained CNN models with effective accuracy and performance. In [35], the authors introduced an effective eye-tracking approach by using a deep-learning-based method. The location of the face relative to the computer was obtained by detecting color from the infrared LED with OpenCV, and the user's gaze position was inferred by the YOLOv3 model.