Recently, our team published a paper entitled "Self-Calibrating Gaze Estimation with Optical Axes Projection for Head-Mounted Eye Tracking" in IEEE Transactions on Industrial Informatics (IEEE TII, Impact Factor: 11.648), a top journal in the field of computer science. Self-Calibrating Gaze Estimation with Optical Axes Projection for Head-Mounted Eye Tracking".D. student Hanyuan Zhang was the first author, postdoctoral student Zhonghua Wan was the corresponding author, and Professor Shiqian Wu, Master student Zheng Gao and Professor Wenbin Chen were the co-authors.

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., 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 OAP as an eye feature to predict VAP is built. The unknown parameters are calculated separately by linearly aligning OAP patterns to natural and prominent 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 known scene saliency.