Eye contact is a crucial non-verbal interaction modality and plays an important role in our everyday social life. While humans are very sensitive to eye contact, the capabilities of machines to capture a person's gaze are still mediocre. We tackle this challenge and present NITEC, a hand-annotated eye contact dataset for ego-vision interaction. NITEC exceeds existing datasets for ego-vision eye contact in size and variety of demographics, social contexts, and lighting conditions, making it a valuable resource for advancing ego-vision-based eye contact research. Our extensive evaluations on NITEC demonstrate strong cross-dataset performance, emphasizing its effectiveness and adaptability in various scenarios, that allows seamless utilization to the fields of computer vision, human-computer interaction, and social robotics. We make our NITEC dataset publicly available to foster reproducibility and further exploration in the field of ego-vision interaction. https://github.com/thohemp/nitec
@article{arxiv.2311.04505,
title = {NITEC: Versatile Hand-Annotated Eye Contact Dataset for Ego-Vision Interaction},
author = {Thorsten Hempel and Magnus Jung and Ahmed A. Abdelrahman and Ayoub Al-Hamadi},
journal= {arXiv preprint arXiv:2311.04505},
year = {2023}
}
Comments
Accepted at IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024)