Deep learning appearance-based 3D gaze estimation is gaining popularity due to its minimal hardware requirements and being free of constraint. Unreliable and overconfident inferences, however, still limit the adoption of this gaze estimation method. To address the unreliable and overconfident issues, we introduce a confidence-aware model that predicts uncertainties together with gaze angle estimations. We also introduce a novel effectiveness evaluation method based on the causality between eye feature degradation and the rise in inference uncertainty to assess the uncertainty estimation. Our confidence-aware model demonstrates reliable uncertainty estimations while providing angular estimation accuracies on par with the state-of-the-art. Compared with the existing statistical uncertainty-angular-error evaluation metric, the proposed effectiveness evaluation approach can more effectively judge inferred uncertainties' performance at each prediction.

翻译：基于深度学习的外观驱动三维视线估计因其硬件需求低且无约束限制而日益流行。然而，不可靠且过度自信的推理仍制约着该视线估计方法的推广应用。针对不可靠与过度自信问题，我们提出一种置信感知模型，该模型在预测视线角度估计的同时预测不确定性。我们还提出一种基于眼部特征退化与推理不确定性升高之间因果关系的有效性评估方法，用于评估不确定性估计。我们的置信感知模型在提供与现有最优方法相当的角估计精度的同时，展现出可靠的不确定性估计能力。与现有的统计性不确定性-角度误差评估度量相比，所提出的有效性评估方法能更有效地判断每次预测中推断不确定性的表现。