Eye Tracking Calibration on Mobile Devices

ABSTRACT Eye tracking has been widely used in psychology, human-computer interaction and many other fields. Recently, eye tracking based on off-the-shelf cameras has produced promising results, compared to the traditional eye tracking devices. This presents an opportunity to introduce eye tracking on mobile devices. However, eye tracking on mobile devices face many challenges, including occlusion of faces and unstable and changing distance between face and camera. This research project aims to obtain stable and accurate calibration of front-camera based eye tracking in dynamic contexts through the construction of real-world eye-movement datasets, the introduction of novel context-awareness models and improved gaze estimation methods that can be adapted to partial faces.