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- W2944075188 abstract "Appearance-based gaze estimation provides relatively unconstrained gaze tracking. However, subject-indepen-dent models achieve limited accuracy partly due to individual variations. To improve estimation, we propose a novel gaze decomposition method and a single gaze point calibration method, motivated by our finding that the inter-subject squared bias exceeds the intra-subject variance for a subject-independent estimator. We decompose the gaze angle into a subject-dependent bias term and a subject-independent difference term between the gaze angle and the bias. The difference term is estimated by a deep convolutional network. For calibration-free tracking, we set the subject-dependent bias term to zero. For single gaze point calibration, we estimate the bias from a few images taken as the subject gazes at a point. Experiments on three datasets indicate that as a calibration-free estimator, the proposed method outperforms the state-of-the-art methods that use single model by up to $10.0%$. The proposed calibration method is robust and reduces estimation error significantly (up to $35.6%$), achieving state-of-the-art performance for appearance-based eye trackers with calibration." @default.
- W2944075188 created "2019-05-16" @default.
- W2944075188 creator A5050788637 @default.
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- W2944075188 date "2019-05-11" @default.
- W2944075188 modified "2023-09-22" @default.
- W2944075188 title "Appearance-Based Gaze Estimation via Gaze Decomposition and Single Gaze Point Calibration." @default.
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