Self-Calibrating Gaze Estimation With Optical Axes Projection for Head-Mounted Eye Tracking
Hanyuan Zhang, Shiqian Wu, Wenbin Chen, Zheng Gao, Zhonghua Wan
Abstract
Gaze estimation suffers from burdensome personal calibration or complex all-device calibration. Self-calibrating methods can meet this challenge but depend on scenes and sacrifice accuracy. We propose a flexible and accurate gaze estimation approach calibrated implicitly with potential gaze patterns. By constructing an optical axis projection (OAP) plane and a visual axis projection (VAP) plane simultaneously, the optical axis and the visual axis can be represented as 2-D points, i.e., the OAP and VAP, which have a similarity transformation, indicating the linear consistency of OAP patterns with gaze patterns. Hence, a 3-D gaze estimation model using the OAP as an eye feature to predict the VAP is built. The unknown parameters are calculated separately by linearly aligning OAP patterns to natural and easily detectable gaze patterns. Experimental results show that the proposed gaze estimation approach is more accurate than state-of-the-art head-mounted gaze estimation methods, which require explicit calibration or multiscene saliency.