# Design of a Binocular Pupil and Gaze Point Detection System Utilizing High Definition Images
###### Abstract
Edge detectors, morphologic operations, and the angular integral projection function are applied to detect the pupil contour in this algorithm. CNN is a dual convolutional neural network pipeline. In the first stage, the pipeline performs pupil position determination using a convolutional neural network. Then, subregions are derived from the downscaled input image. The second pipeline stage employs another convolutional neural network to refine this pupil position by means of the subregions. These algorithms have contributed to create eye image-based data sets. These data sets include real world scenarios, such as illumination changes, reflections (on glasses), make-up, non-centered eye recording, and physiological eye characteristics. ExCuSe used Swirski data set and provided 17 new hand-labeled data sets. ElSe used Swirski and ExCuse data sets and provided seven new hand-labeled data sets. These studies were performed on approximately 200,000 frames [24]. It is proven that eye gaze systems' accuracies increase when the users' head movements are restricted. To achieve high level accuracy values, many commercial companies use a chin rest to keep the head fixed while the gaze estimation process is performed with a desktop-mounted gaze tracking system. At the beginning of the gaze estimation, users are taken into the calibration process. Generally, users are asked to look at one, four or nine points located on the screen [25; 26]. If the number of the calibration points is augmented, gaze estimation error decreases. Gaze estimation error is still a challenge in which linear or nonlinear functions are applied to overcome. Gaze tracking systems have been extensively exploited in different application areas such as human computer interaction, aviation, medicine, robot control, production tests, augmented reality, psychoanalysis, sports training, computer games, training simulators, laser surgery, flight simulations, product development, fatigue detection, and in the military. Another application area of eye gaze tracking system is pupillometry. Pupillometry is the measurement of eye pupil size. Normally, human pupil size range is 2-8 mm [27]. Additionally, ambient light and environmental factors may affect pupil size changes. Besides that, pupillometry shows tonic and phasic changes in pupil size that play a significant role in observing normal and abnormal functioning of the nervous system [28]. Recent research has revealed that pupil abnormalitiesare correlated with many diseases such as multiple sclerosis, migraines, diabetes, alcoholism, depression, anxiety/panic disorder, Alzheimer's disease, Parkinson's disease, and autism [29]. Different disciplines may take advantage of differences in pupil size and eye movements. Also, eye movement analysis with fast systems are providing new opportunities in psychology, neuroscience, ophthalmology and affective computing. Affective computing has multidisciplinary applications. Automatic detection of human emotional states increases the computer's understanding of the human needs. Eye movements hold essential power for recognizing positive and negative emotions. Affective computing contributes to many areas. Some of these can be listed as follows. The cognitive load of a student can be found using eye tracking. Understanding the affective state of a student allows effective and improved learning. Someone's mood can also be inferred from the eyes. This is currently used to monitor the emotional state of patients. Personalization of commercial products can be enhanced by recognizing the human's mood. Affective computing enhances applications such as virtual reality and smart surveillance. Another point is the automatic recognition of emotions that could also be useful to support psychological studies. Such studies give a baseline for the emotional reaction of healthy subjects, which could be compared and used to diagnose mental disorders [30]. Now it is possible that cars recommend music depending on the human's mood and pull to the side of the road when they sense driver's fatigue.