論文アブストラクト：As devices around us become smart, our gaze is poised to become the next frontier of human-computer interaction (HCI). State-of-the-art mobile eye tracker systems typically rely on eye-model-based gaze estimation approaches, which do not require a calibration. However, such approaches require specialized hardware (e.g., multiple cameras and glint points), can be significantly affected by glasses, and, thus, are not fit for ubiquitous gaze-based HCI. In contrast, regression-based gaze estimations are straightforward approaches requiring solely one eye and one scene camera but necessitate a calibration. Therefore, a fast and accurate calibration is a key development to enable ubiquitous gaze-based HCI. In this paper, we introduce CalibMe, a novel method that exploits collection markers (automatically detected fiducial markers) to allow eye tracker users to gather a large array of calibration points, remove outliers, and automatically reserve evaluation points in a fast and unsupervised manner. The proposed approach is evaluated against a nine-point calibration method, which is typically used due to its relatively short calibration time and adequate accuracy. CalibMe reached a mean angular error of 0.59 (0=0.23) in contrast to 0.82 (0=0.15) for a nine-point calibration, attesting for the efficacy of the method. Moreover, users are able to calibrate the eye tracker anywhere and independently in - 10 s using a cellphone to display the collection marker.