Eye-Gaze to Screen Location Mapping for UI Evaluation of Webpages

Abstract

This paper presents a way to track eye-gaze by using webcam and mapping the eye-gaze data compensating head pose and orientation on the display screen. First, we have shown a blank screen with red dots to 10 individuals and recorded their eye-gaze pattern and head orientation associated with that screen location by automated annotation. Then, we trained a neural network to learn the relationship between eye-gaze and head pose with screen location. The proposed method can map eye-gazes to screen with 68.3% accuracy. Next, by using the trained model to estimate eye gaze on screen, we have evaluated content of a website. This gives us an automated way to evaluate the UI of a website. The evaluation metric might be used with several other metrics to define a standard for web design and layout. This also gives insight to the likes and dislikes, important areas of a website. Also, eye tracking by only a webcam simplifies the matter to use this technology in various fields which might open the future prospect of enormous applications.