Improving the accuracy of low-quality eye tracker

Abstract

Eye tracking, refers to the process of measuring the eye gaze. An eye tracker is a device for measuring the position of eye gaze. The increased accuracy and accessibility of eye-tracking technologies in recent years have made it popular in many applications such as web usability, automotive driving and advertising. Recently, there are also new eye tracking applications appearing in HCI area. For example, eye tracking can be used to help the disabled to use computer efficiently, as they can jump between different applications by moving their eye fixations. However, most traditional hardware eye trackers are inconvenient to deploy in daily life. In recent years, with the rapid advancement in deep learning, some researchers have turned to Convolutional Neural Network (CNN) to do eye tracking, in which the inputs are the images of user’s face or eyes and the output will be the predicted eye gaze coordinate. In this project, I aim to improve an existing eye tracking model iTracker from CSAIL for predicting the eye gaze. Since this model is trained for mobile phone, and I’d like to provide a solution for eye tracking in desktop, so I manage to port this model for computer. Afterwards, I use Kalman Smoother and CNN to process the output of iTracker to improve the accuracy of this model so that it will be capable of handling eye tracking tasks in daily scenarios.