# Gaze Tracking Using an Unmodified Web Camera and Convolutional Neural Network
## 2 Related Work
Furthermore, in [30], the authors trained an iTracker based on CNN network on the GazeCapture dataset; the network takes the eye image and face image with its location associated in face grid. The face-based 2D and 3D gaze estimation was proposed in [37]. The authors used a special weight mechanism that takes the information of different parts of the entire face and learns the pattern using a standard CNN network. In [24], the authors addressed the person and head pose as an independent problem for 3D gaze estimation by using a recurrent convolution network from remote cameras. They combined the eyes region, face, and facial landmark points as an independent input into the CNN network, and they used a many-to-one recurrent network for gaze coordinate prediction. In [38], the authors proposed a so-called _Gazemap_ for gaze estimation, which is an abstract pictorial representation of the eyeball, iris, and pupil at its center. Instead of directly regressing two angles of the eyeball, the authors regressed an intermediate pictorial representation that simplifies the gaze estimation task. For estimating the gaze information from the ocular region of an image, a capsules network was proposed [39]. The authors used the concept of capsules that converts pixel intensities to instantiation parameters of features, which accumulate into a higher level of features as the depth of the network increases, and proposed pose-aware Gaze-Net architecture for gaze estimation. Although several studies are available for gaze estimation, only a few studies have covered gaze estimation using unmodified cameras. For instance, in [40], the authors propose a two-stage gaze estimation method that relies on deep learning methods and logistic regression. Their proposed method can be applied to various mobile platforms without additional hardware devices or systematic prior knowledge. In [28], an artificial neural network is proposed to estimate the gaze coordinate from the standard webcam. Their experiment showed a promising result for the development of low-cost tracking using a standard webcam. Model-based gaze estimation is also proposed by [PERSON] et al. for unmodified tablet computers [41]. They presented an EyeTab method based on a model-based approach for binocular gaze estimation that runs solely on an unmodified tablet. There are some works that are similar to our work. For instance, the authors in [42] designed a differential gaze estimation by training a differential neural network. However, the authors in this paper used calibrated high-quality eye images for training their network, which is different from our method because, in our approach, we used only low-resolution images without calibration. In another work [43], the authors designed a hardware-friendly CNN model that utilizes a minimum computational requirement for efficient gaze estimation on low consumer devices. However, they tested their model on MPIIgaze, the dataset that is a calibrated dataset in which on-screen gaze position is converted to the 3D position in the camera coordinate system using a camera calibration procedure from the OpenCV library. The work most similar to the investigation presented in this paper is [40]. However, the authors of that paper proposed their method solely for mobile devices. Furthermore, they tested their method on a dataset collected from a single device. Contrary to that, the model presented our paper is tested on desktop computers and laptops. Additionally, the dataset was collected from multiple devices so that it can be generalized for other desktop setups.