Learning Individualized Automatic Content Magnification in Gaze-based Interaction

The precision of modern commercial off-the-shelf eye trackers has reached a level sufficient for developing gaze-based applications. In many but not all applications, accuracy even allows for replacing a computer mouse with gaze-bazed pointing. The Multi-Modal Interaction Concept for Efficient input (M²ice) tackles accuracy problems with an on-demand hybrid fisheye magnifier. This paper introduces an image-analysis-based approach that identifies areas on the screen where to automatically activate magnification for improved interaction. It combines the separate actions magnifying and clicking into one seamless action. The approach works by combining OpenCV filters for detection of clickable elements on the screen with a local machine learning algorithm predicting whether magnification is needed based on size and position of screen elements. A user study (n = 28) showed a significant speed increase of 18.50 percent (t(27)=-3.95, p=.0002 at α = .05) with automatic magnification compared to separate shortcuts for clicking and magnification.