Manifold Alignment for Person Independent Appearance-Based Gaze Estimation
By Timo Schneider, Boris Schauerte, Rainer Stiefelhagen. 2014
86 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Human-Computer Interaction
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Tadas Baltrusaitis
- Xucong Zhang
- Andreas Bulling
- Yusuke Sugano
- Mario Fritz
- Peter Robinson
- Louis-philippe Morency
- Erroll Wood
In Electrical and Electronic Engineering
In Human-Computer Interaction
- Xucong Zhang
- Mario Fritz
- Andreas Bulling
- Tadas Baltrusaitis
- Yusuke Sugano
- Peter Robinson
- Erroll Wood
- Sergey I. Nikolenko
Citations in the corpus
- Rendering of Eyes for Eye-Shape Registration and Gaze Estimation (2015, background): “While most previous approaches focused on person-dependent training scenarios which require training data from the target user, recently more attention has been paid to personindependent training [8, 9, 34, 35].”
- Appearance-based gaze estimation in the wild (2015, methodology): “Purely data-driven approaches leverage large amounts of training data to learn gaze estimators that generalise to ar bitrary users without the need for person-specific calibra tion [34, 10, 39] settings.”
- MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation (2017, methodology): “performed a cross-person evaluation on theColumbia dataset [13] with 21 gaze points for one frontal head pose of 56 participants [5].”