NITEC: Versatile Hand-Annotated Eye Contact Dataset for Ego-Vision Interaction
This provides a valuable resource for advancing ego-vision-based eye contact research in fields like computer vision, human-computer interaction, and social robotics, but it is incremental as it builds on existing dataset efforts.
The authors tackled the challenge of limited machine capabilities in capturing gaze by presenting NITEC, a hand-annotated eye contact dataset for ego-vision interaction, which exceeds existing datasets in size and variety and demonstrates strong cross-dataset performance.
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