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：基于深度学习外观的三维凝视估计由于其硬件需求较少且无限制的特点而越来越受欢迎。但是，不可靠和过度自信的推断仍然限制了这种凝视估计方法的应用。为了解决不可靠和过度自信的问题，我们引入了一个置信度感知模型，该模型预测不确定性和凝视角度估计。我们还引入了一种基于眼特征退化和推断不确定性升高之间因果关系的有效性评估方法，以评估不确定性估计能力。我们提出的置信度感知模型展现出可靠的不确定性预测能力，并提供了与最先进水平相当的角度估计准确性。与现有的统计不确定性-角度误差评估度量相比，我们提出的有效性评估方法可以更有效地判断每个预测中推断的不确定性表现。