3D gaze estimation is about predicting the line of sight of a person in 3D space. Person-independent models for the same lack precision due to anatomical differences of subjects, whereas person-specific calibrated techniques add strict constraints on scalability. To overcome these issues, we propose a novel technique, Facial Landmark Heatmap Activated Multimodal Gaze Estimation (FLAME), as a way of combining eye anatomical information using eye landmark heatmaps to obtain precise gaze estimation without any person-specific calibration. Our evaluation demonstrates a competitive performance of about 10% improvement on benchmark datasets ColumbiaGaze and EYEDIAP. We also conduct an ablation study to validate our method.
@article{arxiv.2110.04828,
title = {FLAME: Facial Landmark Heatmap Activated Multimodal Gaze Estimation},
author = {Neelabh Sinha and Michal Balazia and Francois Bremond},
journal= {arXiv preprint arXiv:2110.04828},
year = {2022}
}
Comments
Preprint. Final paper accepted at the 17th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS), virtual, November 2021. 8 pages