FLAME: Facial Landmark Heatmap Activated Multimodal Gaze Estimation
arXiv:2110.04828 · doi:10.1109/AVSS52988.2021.9663816
Abstract
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.
Preprint. Final paper accepted at the 17th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS), virtual, November 2021. 8 pages