MutualEyeContact: A conversation analysis tool with focus on eye contact
This tool addresses the need for efficient analysis of eye contact data for cognitive and social scientists, though it is incremental as it builds on existing technologies.
The researchers tackled the challenge of analyzing eye contact in social interactions by developing MutualEyeContact, a tool that combines eye tracking and face recognition to quickly and accurately process data, aiding scientists in understanding its importance.
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.