EYEDIAP: a database for the development and evaluation of gaze estimation algorithms from RGB and RGB-D cameras

By Kenneth Alberto Funes Mora, Florent Monay, Jean-Marc Odobez. Eye Tracking Research & Application, 2014

418 citations recorded by the source corpus.

Subfields: Ophthalmology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/1fd8c71a8859da611a8fde1cbb2bba1c7cf00b4c

How researchers see this paper

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

In Ophthalmology

1. Andreas Bulling
2. Xucong Zhang
3. Mario Fritz
4. James M. Rehg
5. Yusuke Sugano

In Human-Computer Interaction

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

Citations in the corpus

• Eye Tracking for Everyone (2016, background): “Gaze datasets: There are a number of publicly available gaze datasets in the community [24, 40, 31, 25, 34, 43, 13].”
• Gaze360: Physically Unconstrained Gaze Estimation in the Wild (2019, background): “TabletGaze [11] 2D ∼80 51 Eyes No iTracker [12] 2D ∼100 1,450 Full Partially UT MV [23] 3D ∼50 50 Eyes No Columbia [20] 3D 60 56 Full* No RT-GENE [4] 3D 75 15 Full* No MPIIFaceGaze [31] 3D ∼80 15 Full No EYEDIAP [17] 3D 90 16 Full No Weidenb.”
• MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation (2017, methodology): “Table 1): UT Multiview [6] (top), EYEDIAP [12] (middle), and Columbia [13] (bottom).”
• It’s Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation (2016, methodology): “These findings are in general mirrored for the EYEDIAP dataset shown in Fig.”
• Photo-Realistic Monocular Gaze Redirection Using Generative Adversarial Networks (2019, background): “Other publicly available gaze datasets, such as MPIIGaze [34] or EYEDIAP [7] only provide low-resolution images and would therefore introduce a bias towards low quality images.”
• Detecting Attended Visual Targets in Video (2020, methodology): “used for training is a weighted sum of these two: L = wh · Lh + wf · Lf We initialize the Scene Conv with CNN for scene recognition [51] and the Head Conv with CNN for gaze estimation [15].”
• Appearance-based gaze estimation in the wild (2015, methodology): “Table 1): UT Multiview [39] (top), Eyediap [8] (middle), Smith et al.”