Recalibrating Head-Mounted Eye-Trackers in Gaze Selection

• Main Authors: Wei Wei, 魏瑋
• Other Authors: 陳炳宇
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/2ywz2a

碩士 === 國立臺灣大學 === 資訊管理學研究所 === 106 === In this paper, we present a novel approach of recalibrating head-mounted eye-tracking systems in runtime without deliberating user''s working tasks. By gathering the data points from the user''s working tasks (e.g., selecting a target), we may update the regression model used for mapping the device''s positions to the gaze points of the eye-tracker with the data collected. This approach eliminates the need of an explicit recalibration process. While maintaining the stability of the regression model, the user may need to continue his/her tasks without being interrupted if the mapping quality dropped. Our method is built on a known dwell-based gaze selection method "Multiple Confirm", which highlights a two-step validation on selecting targets. However, the user needs to dwell on the target and another confirmation target to complete the selection procedure, this may stop the user''s manipulation. Based on this, we modify the confirmation target and collect nearby gaze data, which are used for replacing the outdated data in the calibration. Then, a robust model is developed to update the regression model''s different sectors in a sequential order. To evaluate the proposed method, we compared it against the usage of eye-trackers without applying runtime recalibration. The result indicates that the accuracy bias of the model can be controlled within a certain degree of visual angle even in a long-term usage.