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MAGiC: A Multimodal Framework for Analysing Gaze in Dyadic Communication.

Ülkü Arslan Aydın¹, Sinan Kalkan², Cengiz Acartürk¹

¹Cognitive Science Program Middle East Technical University Ankara, Turkey.

Journal of Eye Movement Research

|April 8, 2021

Summary

This summary is machine-generated.

This study introduces MAGiC, a framework for analyzing gaze in dynamic face-to-face communication. It simplifies gaze contact and aversion analysis by integrating eye tracking, audio, and video data, reducing manual effort.

Keywords:

Gaze analysis automatic face detection automatic speech segmentation speech analysis

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Area of Science:

Human-Computer Interaction
Cognitive Science
Computer Vision

Background:

Analyzing dynamic scenes in eye tracking research presents significant challenges.
Understanding gaze behavior in face-to-face communication is crucial for social interaction studies.

Purpose of the Study:

To present MAGiC, an open-source framework for analyzing gaze contact and gaze aversion in dynamic, face-to-face communication.
To integrate and synchronize eye tracking, audio, and video data for comprehensive communication analysis.

Main Methods:

Developed MAGiC, a framework integrating eye tracking (gaze), audio (speech segmentation), and video (face tracking).
Automated face detection and tracking of the conversation partner.
Overlayed gaze data onto face video and incorporated speech-act annotation.

Main Results:

MAGiC successfully integrates multimodal data streams for analyzing dynamic gaze patterns.
Demonstrated framework usage with publicly available video content and wiki pages.
A pilot study confirmed MAGiC's capability to reduce annotation effort and manual analysis time for dynamic gaze data.

Conclusions:

MAGiC facilitates the analysis of dynamic gaze data in communication contexts.
The framework streamlines the process of understanding gaze behavior during interactions.
Open-source availability promotes wider adoption and further research in eye tracking analysis.