Quick Bootstrapping of a Personalized Gaze Model from Real-Use Interactions.

Understanding human visual attention is essential for understanding human cognition, which in turn benefits human--computer interaction. Recent work has demonstrated a Personalized, Auto-Calibrating Eye-tracking (PACE) system, which makes it possible to achieve accurate gaze estimation using only an off-the-shelf webcam by identifying and collecting data implicitly from user interaction events. However, this method is constrained by the need for large amounts of well-annotated data. We thus present fast-PACE, an adaptation to PACE that exploits knowledge from existing data from different users to accelerate the learning speed of the personalized model. The result is an adaptive, data-driven approach that continuously “learns” its user and recalibrates, adapts, and improves with additional usage by a user. Experimental evaluations of fast-PACE demonstrate its competitive accuracy in iris localization, validity of alignment identification between gaze and interactions, and effectiveness of gaze transfer. In general, fast-PACE achieves an initial visual error of 3.98 degrees and then steadily improves to 2.52 degrees given incremental interaction-informed data. Our performance is comparable to state-of-the-art, but without the need for explicit training or calibration. Our technique addresses the data quality and quantity problems. It therefore has the potential to enable comprehensive gaze-aware applications in the wild.