# Gaze Tracking Using an Unmodified Web Camera and Convolutional Neural Network
## 2 Related Work  
Gaze estimation has been studied for decades due to its variety of applications in different domains. The traditional approach is to record an image of an eye, use image processing techniques to find a pupil, sclera, light source reflections (Purkinje images), and then use their locations to estimate gaze. The main problem with this approach is that it requires a high-quality image of the eye. Therefore, professional video-based (VOG) eye trackers are equipped with high-quality cameras and have many restrictions for participants (such as using a chin-rest or bite-bar) to ensure that an eye image is visible in the camera. Gaze estimation methods can be categorized into model-based, also known as feature-based, and appearance-based approaches [14]. Model-based methods make use of 3D geometric eye models to estimate gaze direction [15; 16]. They usually explore the characteristics of the human eye to identify a set of distinctive features of the eyes. The limbus, pupil (dark/bright pupil images), and cornea reflections are common features used to estimate gaze direction. Furthermore, these methods also require infrared light sources together with high-resolution cameras. Although this approach can accurately estimate gaze direction, it requires specialized hardware that limits its range of application. If the iris contour alone is used to detect the line of sight, the need for specialized eyewear can be relaxed [17]. In conclusion, these methods are effective for short-distance scenarios in which high-resolution observations are available, but their effectiveness in mid-distance scenarios is unclear. Most commercial eye trackers ordinarily depend on active illumination, i.e., pupil center corneal reflections (PCCR) based eye-tracker uses infrared illumination, which provides high accuracy [18]. However, these types of eye trackers are expensive and generally used in a laboratory setting together with a chin rest or bite bar that stabilizes the head. To reduce the restriction on head movement, the authors proposed different solutions [19; 20]. However, these eye trackers do not work well in outdoor situations because of the sun's infrared radiation. Appearance-based methods have achieved great success in gaze estimation in recent years due to their ability to learn the gaze direction directly from the image of the eye without any feature extraction. These methods require only off-the-shelf cameras, which are normally available, that simplify capturing gaze coordinates in indoor and outdoor conditions. However, attaining a high accuracy is challenging due to many factors such as changes in appearance, lighting conditions, and head pose [21]. With the success of convolutional neural networks (CNNs) in computer vision, researchers have adopted using CNN in appearance-based gaze estimation, which has reduced estimation errors [22]. Trained with high quality diverse real and synthesized datasets covering a wide range of variation [22; 23], a deep CNN network can learn to compensate much of the variability present in the dataset [13; 24]. Deep learning is a powerful technique that has developed fast and is widely used in many applications [25; 26] including computer vision [27] and gaze estimation [28; 29]. Furthermore, there have been several open-source datasets for gaze estimation from the research community, for instance, MPIIGaze [22], GazeCapture [30], TableGaze [31], including head pose and gaze database [32], ETH-XGaze [33], and RT-GENE [34]. Several approaches have been proposed for appearance-based gaze estimation. For instance, in [22], the authors proposed a multimodal convolutional neural network, but before sending input to this network, the authors used the SURF cascade method [35] and constrained local mode framework to detect faces and facial landmark [36]. Furthermore, the authors also used a space normalization technique to normalize the input image and head pose space into a polar coordinate angle space [23].