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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 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.
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.
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