# The Effect of Different Deep Network Architectures upon CNN-Based Gaze Tracking
## 1 Introduction
Although both methods are useful, we observed the incorrect predictions and found that the incorrect estimated class is usually near the correct class. Therefore, we infer that when the user views the edges in two adjacent blocks, it may cause the CNN model to make an incorrect estimation. In general, users watch videos or animations and are interested in objects. For establishing a relationship between an area-of-interest and an interesting object, according to our previous work [18], the ratio of each area-of-interest is the probability of the interest in an object. Thus, when the amount of probability is calculated, one can estimate the amount of attention paid to the object of interest. Even if it is misidentified as a neighboring block, it may still correspond to the same object. Especially when the block area is relatively small, the misjudgment will be higher, and the influence will be reduced by using the classification method. Thus, this paper treats the eye gaze estimation task as a classification problem. By adding convolution and pooling layers compared to traditional neural networks [19], the network can maintain the shape of the image information and reduce parameters. The convolution neural network for the research of gaze tracking can bring great results since convolutional neural networks capture subtler features through deep networks and make a robust model. Several papers have clearly helped improve CNN performance [20; 21; 22; 23]. [PERSON] et al. [20] proposed the method of batch normalization (BN) to overcome the problem of hard to train models with saturating nonlinearities by making normalization a part of the model architecture and performing the normalization for each training mini-batch. The BN can improve the learning speed, reduce the dependence on initial parameters, and eliminate the need for Dropout layer in some cases. GoogleNet [21] is proposed by [PERSON] et al. who increased the depth and width of the network while keeping the computational budget constant, to perform multi-scale processing on images and greatly reduced the amount of model parameters. [PERSON] et al. [22] studied very deep convolutional networks by fixing other parameters and adding more convolutional layers. They used very small (\(3\times 3\)) convolution filters in all layers. This is beneficial for the classification accuracy, and good performance on the ImageNet challenge dataset can be achieved. [PERSON] et al. [23] proposed using the global average pooling (GAP) layer, over feature maps in the classification layer, instead of the fully connected layer. The results have shown that it is easier to interpret and less prone to overfitting. The objective of most of the above research is concentrated on recognizing large sized images. In the case of gaze tracking, the input image usually is the eye or face, however, few studies focus on small sized image with relatively few features. The images (static stimuli) are used to construct the training dataset [11; 12]. However, most visual behavior of many people involves watching dynamic stimuli such as movies from YouTube and Netflix. In this paper, we propose a gaze tracking method with feeding different types of images, including the single eye, double eyes and face based on CNN. Additionally, gaze prediction is considered a classification problem by dividing the screen into blocks to label the gaze. In order to obtain the fed images with gaze label for feature learning in a way that is close to the viewer's visual behavior, the data collection involves participants watching the videos. Performing this way is closer to the actual visual behavior of the viewer rather than letting the participants watch specific screen blocks such as [13]. The training images then are fed into the convolution neural network for training prediction model, performing several experiments by adjusting network parameters or architecture to explore the performance of the model.