Evaluation of appearance-based eye tracking calibration data selection

Eye tracking is a valuable topic in computer vision. Appearance-based eye tracking is a promising research direction in recent years. Convolutional neural networks (CNN) had been used in gaze estimation, which cover the significant variability in eye appearance caused by unconstrained head motion. With computation capability of consumer devices rapidly evolving, accurate and efficient appearance-based eye tracking has the potential for multipurpose applications. Person-independent networks have limit in improving gaze estimation accuracy. Person-specific network with calibration is more effective than person-independent approaches. Unlike classical eye tracking methods, appearance-based eye tracking has not a clear way to calibration. Our goal is to analyze the impact of calibration data selection and calibration target distribution on person-specific gaze estimation accuracy. We trained person-independent network and use SVR to calibration. We choose two kind of typical distribution targets to evaluation. Use different distribution targets to calibration achieves different accuracy.