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• W2087806427 abstract "We show that dually supervised manifold embedding can improve the performance of machine learning based person-independent and thus calibration-free gaze estimation. For this purpose, we perform a manifold embedding for each person in the training dataset and then learn a linear transformation that aligns the individual, person-dependent manifolds. We evaluate the effect of manifold alignment on the recently presented Columbia dataset, where we analyze the influence on 6 regression methods and 8 feature variants. Using manifold alignment, we are able to improve the person-independent gaze estimation performance by up to 31.2 % compared to the best approach without manifold alignment." @default.
• W2087806427 created "2016-06-24" @default.
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• W2087806427 date "2014-08-01" @default.
• W2087806427 modified "2023-10-16" @default.
• W2087806427 title "Manifold Alignment for Person Independent Appearance-Based Gaze Estimation" @default.
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• W2087806427 doi "https://doi.org/10.1109/icpr.2014.210" @default.
• W2087806427 hasPublicationYear "2014" @default.
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