Combining Head Pose and Eye Location Information for Gaze Estimation

By Roberto Valenti, Nicu Sebe, Theo Gevers. IEEE Transactions on Image Processing, 2012

359 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/c507db2cef1889d6c93f648d3573a51d9426daf0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Antonio Torralba
2. Mario Fritz
3. Yusuke Sugano
4. Xucong Zhang
5. Aditya Khosla
6. Andreas Bulling

In Human-Computer Interaction

1. Xucong Zhang
2. Mario Fritz
3. Andreas Bulling
4. Yusuke Sugano
5. Antonio Torralba
6. Wojciech Matusik
7. Petr Kellnhofer
8. Sergey I. Nikolenko

Citations in the corpus

• Eye Tracking for Everyone (2016, methodology): “On the other hand, shape-based methods [15, 4, 39, 9] infer gaze direction from observed eye shapes, such as pupil centers and iris edges.”
• Manifold Alignment for Person Independent Appearance-Based Gaze Estimation (2014, neutral): “, [3]), or iris contour (e.”
• It’s Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation (2016, methodology): “Usually p is directly defined in the coordinate system of the target screen [17, 26, 27, 29] or, more generally, a virtual plane defined in the camera coordinate system [13].”
• MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation (2017, neutral): “Shape-based methods [29], [30], [31], [32]”
• Appearance-based gaze estimation in the wild (2015, background): “In contrast, shape-based meth-ods [16, 4, 50, 44] directly infer gaze directions from observed eye shapes, such as pupil centre or iris edges.”
• Adaptive Linear Regression for Appearance-Based Gaze Estimation (2014, neutral): “The most commonly extracted features include corneal infrared reflections [1], [2], pupil center [3], and iris contour [4].”