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In:Eye-tracking in Interaction: Studies on the role of eye gaze in dialogue
Edited by Geert Brône and Bert Oben
[Advances in Interaction Studies 10] 2018
► pp. 139–168

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Chapter 7
Gaze and face-to-face interaction

From multimodal data to behavioral models

Published online: 13 November 2018
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https://doi.org/10.1075/ais.10.07bai
Abstract
This chapter describes experimental and modeling work aiming at describing gaze patterns that are mutually exchanged by interlocutors during situated and task-directed face-to-face two-ways interactions. We will show that these gaze patterns (incl. blinking rate) are significantly influenced by the cognitive states of the interlocutors (speaking, listening, thinking, etc.), their respective roles in the conversation (e.g. instruction giver, respondent) as well as their social relationship (e.g. colleague, supervisor).
This chapter provides insights into the (micro-)coordination of gaze with other components of attention management as well as methodologies for capturing and modeling behavioral regularities observed in experimental data. A particular emphasis is put on statistical models, which are able to learn behaviors in a data-driven way.
We will introduce several statistical models of multimodal behaviors that can be trained on such multimodal signals and generate behaviors given perceptual cues. We will notably compare performances and properties of models which explicitly model the temporal structure of studied signals, and which relate them to internal cognitive states. In particular we study Semi-Hidden Markov Models and Dynamic Bayesian Networks and compare them to classifiers without sequential models (Support Vector Machines and Decision Trees).
We will further show that the gaze of conversational agents (virtual talking heads, speaking robots) may have a strong impact on communication efficiency. One of the conclusions we draw from these experiments is that multimodal behavioral models able to generate co-verbal gaze patterns of interactive avatars should be designed with great care in order not to increase the cognitive load of human partners. Experiments involving an impoverished or irrelevant control of the gaze of artificial agents (virtual talking heads and humanoid robots) have demonstrated its negative impact on communication (Garau, Slater, Bee, & Sasse, 2001).
Article outline
  • 1.Introduction
  • 2.Interactive gaze
    • 2.1Eyes in the visual scene
    • 2.2Conversational gaze
    • 2.3Mutual gaze patterns
  • 3.Learning & generating gaze patterns
    • 3.1Grounding gaze patterns
    • 3.2Learning joint behaviors
    • 3.3A sample interactive game
    • 3.4Learning joint behaviors with dynamic Bayesian networks
    • 3.5Adapting joint behaviors
    • 3.6Effective gaze tracking and generation
  • 4.Active gaze estimation from images and videos: Gaze patterns and interaction models
  • 5.Easing gaze reading
    • 5.1Eye appearance
    • 5.2Estimating gaze direction of avatars
  • 6.Future trends
  • Acknowledgments
  • Note
  • References
References (111)
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