# MutualEyeContact: A conversation analysis tool with focus on eye contact

arXiv: https://arxiv.org/abs/2107.04476 · HTML: https://mlsanity.com/paper/2107.04476/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 9 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [Structure from Articulated Motion: Accurate and Stable Monocular 3D Reconstruction without Training Data](https://mlsanity.com/paper/1905.04789/) (2019)

## Abstract (arXiv)

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.