  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.
