MAGiC: A Multimodal Framework for Analysing Gaze in Dyadic Communication

Aydin U. A., Kalkan S., Acartürk C.

JOURNAL OF EYE MOVEMENT RESEARCH, vol.11, 2018 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 11
  • Publication Date: 2018
  • Doi Number: 10.16910/jemr.11.6.2
  • Journal Name: JOURNAL OF EYE MOVEMENT RESEARCH
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: Gaze analysis, speech analysis, automatic face detection, automatic speech segmentation, CUES, EYES
  • Middle East Technical University Affiliated: Yes

Abstract

The analysis of dynamic scenes has been a challenging domain in eye tracking research. This study presents a framework, named MAGiC, for analyzing gaze contact and gaze aversion in face-to-face communication. MAGiC provides an environment that is able to detect and track the conversation partner’s face automatically, overlay gaze data on top of the face video, and incorporate speech by means of speech-act annotation. Specifically, MAGiC integrates eye tracking data for gaze, audio data for speech segmentation, and video data for face tracking. MAGiC is an open source framework and its usage is demonstrated via publicly available video content and wiki pages. We explored the capabilities of MAGiC through a pilot study and showed that it facilitates the analysis of dynamic gaze data by reducing the annotation effort and the time spent for manual analysis of video data.

The analysis of dynamic scenes has been a challenging domain in eye tracking research. This study presents a framework, named MAGiC, for analyzing gaze contact and gaze aversion in face-to-face communication. MAGiC provides an environment that is able to detect and track the conversation partner's face automatically, overlay gaze data on top of the face video, and incorporate speech by means of speech-act annotation. Specifically, MAGiC integrates eye tracking data for gaze, audio data for speech segmentation, and video data for face tracking. MAGiC is an open source framework and its usage is demonstrated via publicly available video content and wild pages. We explored the capabilities of MAGiC through a pilot study and showed that it facilitates the analysis of dynamic gaze data by reducing the annotation effort and the time spent for manual analysis of video data.