# Gaze Tracking Using an Unmodified Web Camera and Convolutional Neural Network
## 1 Introduction  
Gaze estimation has long been recognized as an important research topic since it has strong real-world applications, for instance, in human-computer interfaces [1, 2], gaze-based interfaces [3, 4], virtual reality [5, 6], health care [7], behavioral analysis [8], and communication skills [9]. Therefore, gaze estimation has become a well-established research topic in computer vision, especially in human-computer interaction (HCI) [10, 11]. Early gaze estimation techniques required strict conditions to calculate gaze points, such as stabilizing a person's head and controlling light conditions. These restraints bounded the applications to relatively restricted laboratory environments. For applying gaze estimation in natural environments, we have proposed many methods to mitigate these restraints and have uplifted gaze estimation towards being calibration-free, without person-specific and light independent gaze tracking [12, 13]. Most of the available eye-tracking methods are expensive as they require specialized commercial hardware and software. With the growing number of applications that use eye-tracking, there is also a growing need for cheap and ubiquitous methods to obtain information about human gaze coordinates. Therefore, methods utilizing unmodified cameras currently built into almost every computer setup can play an essential role in popularizing eye-tracking since they are easily available and do not require additional costs. Unmodified web cameras have been used in this paper to assess the possibility of utilizing them to estimate gaze coordinates. Nowadays, almost every computer is equipped with a camera that potentially may be used for gaze estimation. The main problem is that unmodified web cameras work only in visible light (and most have infrared filters). The next problem is that they typically have a wide-angle lens with limited (if any) possibilitiesto zoom. The result is that the image of the eyes is small (low resolution) and is highly dependent on light conditions. That is why using an unmodified camera for eye tracking is a big challenge. Algorithms that use unmodified cameras are different from classical eye tracking algorithms, which utilize infrared cameras and an additional infrared light source that produces a glint (reflection from eyeball) to track the eyes. Although cameras with infrared light can detect eyes with high accuracy, they add an additional cost to the eye-tracking. In our research, we used standard unmodified cameras to detect face and eye regions, which reduces the eye-tracking cost significantly. Estimating the gaze coordinate from the unmodified cameras is a difficult task as compared to infrared cameras because, in unmodified cameras, the pupil cannot be located clearly all the time. In addition, different illumination conditions, head position, and eye angles can make it hard to estimate the person's gaze. In this research, the image taken by an unmodified web camera was fed to the state-of-the-art CNN network to produce the information about the region on a screen where the person was looking. The CNN network was trained and tested with several different architectures of the CNN network with several combinations of face, both eyes, and single eyes to estimate the gaze coordinate from the camera images. Our experimental findings show that it is possible to predict human gaze points from an unmodified camera with reasonable accuracy. The main contribution of the paper is creating and testing a dataset that was collected in the wild by people sitting at their own laptops without any supervision. This dataset was used to train different networks. The low quality of the recordings made it challenging to assess the exact coordinates of the gaze points, but it was shown that it is possible to estimate with high accuracy the 20 screen areas the person is looking at. The rest of the paper is structured as follows: Section 2 presents related work for the gaze estimation. Section 3 presents our dataset and the different network architectures used in this study.