Investigating automatic dominance estimation in groups from visual attention and speaking activity

Authors:
Hayley Hung;Dinesh Babu Jayagopi;Sileye Ba;Jean-Marc Odobez;Daniel Gatica-Perez
Affiliations:
Idiap Research Institute, Martigny, Switzerland;Ecole Polytechnique Federale de Lausanne and Idiap Research Institute, Martigny, Switzerland;Idiap Research Institute, Martigny, Switzerland;Ecole Polytechnique Federale de Lausanne and Idiap Research Institute, Martigny, Switzerland;Ecole Polytechnique Federale de Lausanne and Idiap Research Institute, Martigny, Switzerland
Venue:
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Year:
2008

Citing 6
Cited 12

Explaining effects of eye gaze on mediated group conversations:: amount or synchronization?

CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
Quantifying interpersonal influence in face-to-face conversations based on visual attention patterns

CHI '06 Extended Abstracts on Human Factors in Computing Systems
Using audio and video features to classify the most dominant person in a group meeting

Proceedings of the 15th international conference on Multimedia
Influencing social dynamics in meetings through a peripheral display

Proceedings of the 9th international conference on Multimodal interfaces
Modeling dominance in group conversations using nonverbal activity cues

IEEE Transactions on Audio, Speech, and Language Processing - Special issue on multimodal processing in speech-based interactions
The AMI meeting corpus: a pre-announcement

MLMI'05 Proceedings of the Second international conference on Machine Learning for Multimodal Interaction

Predicting two facets of social verticality in meetings from five-minute time slices and nonverbal cues

ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Automatic nonverbal analysis of social interaction in small groups: A review

Image and Vision Computing
Putting the pieces together: multimodal analysis of social attention in meetings

Proceedings of the international conference on Multimedia
3D user-perspective, voxel-based estimation of visual focus of attention in dynamic meeting scenarios

International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
Automatic detection of dominance and expected interest

EURASIP Journal on Advances in Signal Processing
VlogSense: Conversational behavior and social attention in YouTube

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
Automatic modeling of dominance effects using granger causality

HBU'11 Proceedings of the Second international conference on Human Behavior Unterstanding
Linking speaking and looking behavior patterns with group composition, perception, and performance

Proceedings of the 14th ACM international conference on Multimodal interaction
On the relationship between head pose, social attention and personality prediction for unstructured and dynamic group interactions

Proceedings of the 15th ACM on International conference on multimodal interaction
A semi-automated system for accurate gaze coding in natural dyadic interactions

Proceedings of the 15th ACM on International conference on multimodal interaction
3D head pose and gaze tracking and their application to diverse multimodal tasks

Proceedings of the 15th ACM on International conference on multimodal interaction
A dominance estimation mechanism using eye-gaze and turn-taking information

Proceedings of the 6th workshop on Eye gaze in intelligent human machine interaction: gaze in multimodal interaction

Quantified Score

Hi-index	0.00

Visualization

Abstract

We study the automation of the visual dominance ratio (VDR); a classic measure of displayed dominance in social psychology literature, which combines both gaze and speaking activity cues. The VDR is modified to estimate dominance in multi-party group discussions where natural verbal exchanges are possible and other visual targets such as a table and slide screen are present. Our findings suggest that fully automated versions of these measures can estimate effectively the most dominant person in a meeting and can match the dominance estimation performance when manual labels of visual attention are used.