Eye-gaze estimation under various head positions and iris states

•A new method for eye-gaze estimation under normal head movement is investigated.•Head position and orientations are acquired by Kinect depth data and eye direction is obtained from high resolution images.•Bayesian multinomial logistic regression is used to construct a gaze mapping function and to verify iris state.•The features used for learning, vary with different head positions and orientations and eye directions.•Whenever the user moves away from the camera, the gaze score will decrease.

This paper describes a method for eye-gaze estimation under normal head movement. In this method, head position and orientation are acquired by Kinect depth data and eye direction is obtained from high resolution images. We propose the Bayesian multinomial logistic regression based on a variational approximation to construct a gaze mapping function and to verify iris state. Our method eliminates limitation of head movements, eye closure and light source as common drawbacks in most conventional techniques. The efficiency of the proposed method is validated by performance evaluation for multiple people with different distances and poses to the camera under various eye states.