Automatic mutual gaze detection in face-to-face dyadic interaction videos
By C. Palmero, E. V. Dam, Sergio Escalera, M. Kelia, G. Lichtert, Lucas P. J. J. Noldus, A. Spink, A. Wieringen. 2018
15 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, 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
- Luc Van Gool
- Andrew Zisserman
- Alan L. Yuille
- Jitendra Malik
- Dacheng Tao
- Xiaogang Wang
- Bernt Schiele
- Ming-Hsuan Yang
In Human-Computer Interaction
- Manuel Jesús Marín-Jiménez
- Xuhui Jia
- James M. Rehg
- Andrew Zisserman
- Yukun Zhu
- Raviteja Vemulapalli
- Liang Wang
- Kristen Grauman
Citations in the corpus
- Boosting Image-based Mutual Gaze Detection using Pseudo 3D Gaze (2020, methodology): “Recently, (Palmero et al. 2018) built a mutual gaze detection model on top of 3D gaze vectors estimated using a CNN and the locations of faces in the image.”
- Detecting Attended Visual Targets in Video (2020, background): “For example, several approaches have been developed to detect if two people are looking at each other [30, 38, 29], or to detect if more than two people are looking at a common target [40, 13].”
- LAEO-Net: Revisiting People Looking at Each Other in Videos (2019, methodology): “In a controlled scenario with just two people interacting, [21] addresses the LAEO problem by using two calibrated cameras placed in front of the two participants, making sure that there is an overlapping visible zone between both cameras.”