Self-Calibrating Gaze Estimation via Matching Spatio-Temporal Reading Patterns and Eye-Feature Patterns

Self-calibrating gaze estimation eliminates burdensome personal calibration by utilizing gaze behaviors to calibrate gaze estimation models automatically. However, they rely on viewing multiple specific scenes or sacrifice accuracy. Due to the pervasiveness of reading, we propose a pervasive and accurate self-calibrating approach that requires reading only a few lines of text naturally. This approach reformulates the calibration model as nonlinearly matching spatio-temporal reading patterns and the corresponding eye-feature patterns. The eye features are filtered into fixations by filtering nonreading data and saccades to ensure visual intake. Fixations are segmented into multiple lines by recovering the temporal reading pattern, thus simplifying the nonlinear matching into the line-to-line matching between segmented fixations and text lines, which is achieved by recovering the spatial reading pattern. Experimental results show that the proposed approach has comparable accuracy to state-of-the-art head-mounted gaze estimation methods, which require explicit calibration or multiple salient scenes.