Adaptive eye-camera calibration for head-worn devices

We present a novel, continuous, locally optimal calibration scheme for use with head-worn devices. Current calibration schemes solve for a globally optimal model of the eye-device transformation by performing calibration on a per-user or once-per-use basis. However, these calibration schemes are impractical for real-world applications because they do not account for changes in calibration during the time of use. Our calibration scheme allows a head-worn device to calculate a locally optimal eye-device transformation on demand by computing an optimal model from a local window of previous frames. By leveraging naturally occurring interest regions within the user´s environment, our system can calibrate itself without the user´s active participation. Experimental results demonstrate that our proposed calibration scheme outperforms the existing state of the art systems while being significantly less restrictive to the user and the environment.