# The Effect of Different Deep Network Architectures upon CNN-Based Gaze Tracking
## 1 Introduction  
Gaze tracking can help understand cognitive processes and emotional state, and has been applied in many fields, such as medicine, Human-Computer Interaction (HCI), and e-learning [1; 2; 3]. The techniques of gaze tracking are classified into two methods, model-based and appearance-based [4]. First, the model-based method mainly uses the near-infrared light device to track the pupil position and the designed algorithm to estimate the gaze points which usually require expensive hardware [5]. A simple video-based eye tracking system was developed with one camera and one infrared light source to determine a person's point of regard (PoR) [6], assuming the location of features in the eye video is known. [PERSON] et al. [7] used the dynamic head compensation model to solve the effect of head movement for estimating the gaze movement. [PERSON] et al. [8] used the Kinect sensor to detect the three-dimensional coordinates of the head movement. Then the gaze is calculated byusing the estimated eye model parameters acquired from gradients iris center localization method, geometric constraints-based and Kappa angle calculation method. Second, the appearance-based method mainly uses the technology of machine learning to gain the features of a large number of input samples, such as eye, eyes or face, and then used the learning model to predict the gaze [9]. [PERSON] et al. [10] proposed two procedures to estimate the gazing direction. First, the eye region is located by modifying the characteristics of the Active Appearance Model. Then the five gazing direction classes are predicted by employing the Support Vector Machine (SVM). Because deep learning (DL) and Convolution Neural Network (CNN) has a prominent performance in computer vision, there are some studies that are used to improve the accuracy of eye movement prediction considering as a regression task [11; 12; 13; 14; 15] or a classification task [11; 16]. The authors applied the convolutional neural network and trained a regression model in the output layer for gaze estimation [11]. [PERSON] et al. developed the GazeCapture for gaze prediction, the first large-scale eye tracking dataset captured via crowdsourcing and iTracker, a convolutional neural network, is trained [12]. [PERSON] et al. [13] proposed estimating the gaze angle by dividing the screen area into 41 points and random forest regression is used. In [14], a convolutional neural network model was introduced as a regression problem with finding a gaze angle. This model has low computational requirements, and effective appearance-based gaze estimation was performed on it. [PERSON] et al. [15] proposed a gaze estimation method in which a CNN utilizes the full face image as input with spatial weights on the feature maps. Based on a CNN classification task, the eye image is input to the multi-scale convolutional layer for depth feature extraction. The gaze direction classification is treated as a multi-class classification problem [16]. The left and right eyes are trained separately by two convolutional neural networks to classify the gaze in seven directions. The scores from both the networks are used to obtain the class labels. [PERSON] et al. [11] created the MPIIGaze dataset that contains 213,659 images collected from 15 participants. First, the head rotation angle and eye coordinates of the facial image are obtained through the head pose model and facial feature model. Then, the multimodal CNN is used to learn the mapping from the head poses and eye images to gaze directions in the camera coordinate system. [PERSON] et al. [17] did not directly estimate the gaze angle, but introduced a method to divide human gaze into nine directions, and established a convolutional neural network model to estimate directions for a screen typing application. Many studies predicted the coordinates of the gaze point by the regression method. The accuracy of gaze estimation is expressed as the error of gaze direction or coordinate angle (degree).