FLAME: Facial Landmark Heatmap Activated Multimodal Gaze Estimation
This addresses the trade-off between precision and scalability in gaze estimation for applications like human-computer interaction, though it appears incremental.
The paper tackled the problem of 3D gaze estimation by proposing FLAME, a method that uses facial landmark heatmaps to combine eye anatomical information, achieving about a 10% improvement on benchmark datasets without person-specific calibration.
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.