MutualEyeContact: A conversation analysis tool with focus on eye contact

TL;DR

MutualEyeContact is a tool that uses eye tracking and machine learning to automatically analyze and visualize mutual eye contact in social interactions, aiding behavioral research.

Contribution

The paper introduces a novel tool combining eye tracking, face recognition, and visualization for automatic analysis of mutual eye contact in social interactions.

Findings

Accurate automatic detection of mutual eye contact.

Enhanced analysis speed for social interaction data.

Integration of computer vision with behavioral science.

Abstract

Eye contact between individuals is particularly important for understanding human behaviour. To further investigate the importance of eye contact in social interactions, portable eye tracking technology seems to be a natural choice. However, the analysis of available data can become quite complex. Scientists need data that is calculated quickly and accurately. Additionally, the relevant data must be automatically separated to save time. In this work, we propose a tool called MutualEyeContact which excels in those tasks and can help scientists to understand the importance of (mutual) eye contact in social interactions. We combine state-of-the-art eye tracking with face recognition based on machine learning and provide a tool for analysis and visualization of social interaction sessions. This work is a joint collaboration of computer scientists and cognitive scientists. It combines the fields of social and behavioural science with computer vision and deep learning.