A robust, real-time camera-based eye gaze tracking system to analyze users’ visual attention using deep learning

Eye gaze tracking has recently become indispensable for domains like virtual reality, augmented reality, human–computer interaction and advertisement. The commercial eye gaze tracking equipment is too expensive to be used by the masses. In this manuscript, a non-invasive, low-resolution ordinary camera-based system has been proposed for tracking eye gaze. The proposed system attained an accuracy of 84% without requiring any costly equipment or infrared light like other commercial tools. Eye gaze fixations data of 37 participants were recorded on a webpage and gaze patterns were plotted using heat maps. Further, a case analysis was done to validate the performance of the proposed system, where the user interface has been controlled using eye movements.