Adaptive Eye-Camera Calibration for Head-Worn Devices

David Perra, Rohit Kumar Gupta, Jan-Michael Frahm; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 4146-4155

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.