research-article

Conversation scene analysis based on dynamic Bayesian network and image-based gaze detection

Authors:

Sebastian Gorga,

Kazuhiro OtsukaAuthors Info & Claims

ICMI-MLMI '10: International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction

Article No.: 54, Pages 1 - 8

https://doi.org/10.1145/1891903.1891969

Published: 08 November 2010 Publication History

Abstract

This paper presents a probabilistic framework, which incorporates automatic image-based gaze detection, for inferring the structure of multiparty face-to-face conversations. This framework aims to infer conversation regimes and gaze patterns from the nonverbal behaviors of meeting participants, which are captured from image and audio streams with cameras and microphones. The conversation regime corresponds to a global conversational pattern such as monologue and dialogue, and the gaze pattern indicates "who is looking at whom". Input nonverbal behaviors include presence/absence of utterances, head directions, and discrete head-centered eye-gaze directions. In contrast to conventional meeting analysis methods that focus only on the participant's head pose as a surrogate of visual focus of attention, this paper newly incorporates vision-based gaze detection combined with head pose tracking into a probabilistic conversation model based on dynamic Bayesian network. Our gaze detector is able to differentiate 3 to 5 different eye gaze directions, e.g. left, straight and right. Experiments on four-person conversations confirm the power of the proposed framework in identifying conversation structure and in estimating gaze patterns with higher accuracy then previous models.

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Cited By

Gupta ATafasca SOdobez J(2022)A Modular Multimodal Architecture for Gaze Target Prediction: Application to Privacy-Sensitive Settings2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW56347.2022.00552(5037-5046)Online publication date: Jun-2022
https://doi.org/10.1109/CVPRW56347.2022.00552
Siegfried ROdobez J(2021)Visual Focus of Attention Estimation in 3D Scene with an Arbitrary Number of Targets2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW53098.2021.00352(3147-3155)Online publication date: Jun-2021
https://doi.org/10.1109/CVPRW53098.2021.00352
Otsuka KKasuga KKöhler MD'Mello SGeorgiou PScherer SProvost ESoleymani MWorsley M(2018)Estimating Visual Focus of Attention in Multiparty Meetings using Deep Convolutional Neural NetworksProceedings of the 20th ACM International Conference on Multimodal Interaction10.1145/3242969.3242973(191-199)Online publication date: 2-Oct-2018
https://dl.acm.org/doi/10.1145/3242969.3242973
Show More Cited By

Index Terms

Conversation scene analysis based on dynamic Bayesian network and image-based gaze detection
1. Computing methodologies
  1. Artificial intelligence
    1. Philosophical/theoretical foundations of artificial intelligence
      1. Cognitive science
2. Human-centered computing
  1. Human computer interaction (HCI)

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Information

Published In

cover image ACM Conferences

ICMI-MLMI '10: International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction

November 2010

311 pages

ISBN:9781450304146

DOI:10.1145/1891903

General Chairs:
Wen Gao
PKU, China
,
Chin-Hui Lee
Georgia Tech
,
Jie Yang
Carnegie Mellon
,
Program Chairs:
Xilin Chen
ICT, CAS, China
,
Maxine Eskenazi
Carnegie Mellon
,
Zhengyou Zhang
MSR

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 November 2010

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ICMI-MLMI '10

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SIGCHI

ICMI-MLMI '10: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERFACES/WORKSHOP ON MACHINE LEARNING FOR MULTIMODAL INTERFACES

November 8 - 10, 2010

Beijing, China

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ICMI-MLMI '10 Paper Acceptance Rate 41 of 100 submissions, 41%;

Overall Acceptance Rate 453 of 1,080 submissions, 42%

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Cited By

Gupta ATafasca SOdobez J(2022)A Modular Multimodal Architecture for Gaze Target Prediction: Application to Privacy-Sensitive Settings2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW56347.2022.00552(5037-5046)Online publication date: Jun-2022
https://doi.org/10.1109/CVPRW56347.2022.00552
Siegfried ROdobez J(2021)Visual Focus of Attention Estimation in 3D Scene with an Arbitrary Number of Targets2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW53098.2021.00352(3147-3155)Online publication date: Jun-2021
https://doi.org/10.1109/CVPRW53098.2021.00352
Otsuka KKasuga KKöhler MD'Mello SGeorgiou PScherer SProvost ESoleymani MWorsley M(2018)Estimating Visual Focus of Attention in Multiparty Meetings using Deep Convolutional Neural NetworksProceedings of the 20th ACM International Conference on Multimodal Interaction10.1145/3242969.3242973(191-199)Online publication date: 2-Oct-2018
https://dl.acm.org/doi/10.1145/3242969.3242973
Otsuka K(2018)Behavioral Analysis of Kinetic Telepresence for Small Symmetric Group-to-Group MeetingsIEEE Transactions on Multimedia10.1109/TMM.2017.277139620:6(1432-1447)Online publication date: Jun-2018
https://doi.org/10.1109/TMM.2017.2771396
Tian LJia QZhu Z(2018)Predicting Turn-Taking by Compact Gazing Transition Patterns in Multiparty ConversationImage and Video Technology10.1007/978-3-319-75786-5_35(437-447)Online publication date: 15-Feb-2018
https://doi.org/10.1007/978-3-319-75786-5_35
Ding YZhang YXiao MDeng ZMark GFussell SLampe Cschraefel mHourcade JAppert CWigdor D(2017)A Multifaceted Study on Eye Contact based Speaker Identification in Three-party ConversationsProceedings of the 2017 CHI Conference on Human Factors in Computing Systems10.1145/3025453.3025644(3011-3021)Online publication date: 2-May-2017
https://dl.acm.org/doi/10.1145/3025453.3025644
Kumano SOtsuka KIshii RYamato J(2017)Collective First-Person Vision for Automatic Gaze Analysis in Multiparty ConversationsIEEE Transactions on Multimedia10.1109/TMM.2016.260800219:1(107-122)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1109/TMM.2016.2608002
Gatica-Perez DAran OJayagopi D(2017)Analysis of Small GroupsSocial Signal Processing10.1017/9781316676202.025(349-367)Online publication date: 13-Jul-2017
https://doi.org/10.1017/9781316676202.025
Otsuka K(2016)MMSpace: Kinetically-augmented telepresence for small group-to-group conversations2016 IEEE Virtual Reality (VR)10.1109/VR.2016.7504684(19-28)Online publication date: Mar-2016
https://doi.org/10.1109/VR.2016.7504684
Chen YChen JTseng Y(2016)Inference of Conversation Partners by Cooperative Acoustic Sensing in Smartphone NetworksIEEE Transactions on Mobile Computing10.1109/TMC.2015.246537615:6(1387-1400)Online publication date: 1-Jun-2016
https://doi.org/10.1109/TMC.2015.2465376
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