Gaze Tracking and Object Recognition from Eye Images
Lotfi El Hafi, Ming Ding, Jun Takamatsu, Tsukasa Ogasawara · 2017
This paper introduces a method to identify the focused object in eye images captured from a single camera in order to enable intuitive eye-based interactions using wearable devices. Indeed, eye images allow to not only obtain natural user responses from eye movements, but also the scene reflected on the cornea without the need for additional sensors such as a frontal camera, thus making it more socially acceptable. The proposed method relies on a 3D eye model reconstruction to evaluate the gaze direction from the eye images. The gaze direction is then used in combination with deep learning algorithms to classify the focused object reflected on the cornea. Finally, the experimental results using a wearable prototype demonstrate the potential of the proposed method solely based on eye images captured from a single camera.