Accurate Model-Based Point of Gaze Estimation on Mobile Devices

The most accurate remote Point of Gaze (PoG) estimation methods that allow free head movements use infrared light sources and cameras together with gaze estimation models. Current gaze estimation models were developed for desktop eye-tracking systems and assume that the relative roll between the system and the subjects’ eyes (the ’R-Roll’) is roughly constant during use. This assumption is not true for hand-held mobile-device-based eye-tracking systems. We present an analysis that shows the accuracy of estimating the PoG on screens of hand-held mobile devices depends on the magnitude of the R-Roll angle and the angular offset between the visual and optical axes of the individual viewer. We also describe a new method to determine the PoG which compensates for the effects of R-Roll on the accuracy of the POG. Experimental results on a prototype infrared smartphone show that for an R-Roll angle of 90 ° , the new method achieves accuracy of approximately 1 ° , while a gaze estimation method that assumes that the R-Roll angle remains constant achieves an accuracy of 3.5 ° . The manner in which the experimental PoG estimation errors increase with the increase in the R-Roll angle was consistent with the analysis. The method presented in this paper can improve significantly the performance of eye-tracking systems on hand-held mobile-devices