Recently, appearance-based gaze estimation has been attracting attention in
computer vision, and remarkable improvements have been achieved using various
deep learning techniques. Despite such progress, most methods aim to infer gaze
vectors from images directly, which causes overfitting to person-specific
appearance factors. In this paper, we address these challenges and propose a
novel framework: Stochastic subject-wise Adversarial gaZE learning (SAZE),
which trains a network to generalize the appearance of subjects. We design a
Face generalization Network (Fgen-Net) using a face-to-gaze encoder and face
identity classifier and a proposed adversarial loss. The proposed loss
generalizes face appearance factors so that the identity classifier inferences
a uniform probability distribution. In addition, the Fgen-Net is trained by a
learning mechanism that optimizes the network by reselecting a subset of
subjects at every training step to avoid overfitting. Our experimental results
verify the robustness of the method in that it yields state-of-the-art
performance, achieving 3.89 and 4.42 on the MPIIGaze and EyeDiap datasets,
respectively. Furthermore, we demonstrate the positive generalization effect by
conducting further experiments using face images involving different styles
generated from the generative model.

最近，外貌导向的凝视估计成功应用于计算机视觉领域，并且通过各种深度学习技术得到了显著改进。本文提出了一种名为 SAZE 的新框架，通过训练网络来推广主题的外观，达到一致的概率分布推理。使用面部 - 凝视编码器、面部身份分类器和提出的对抗性损失函数设计了 Fgen-Net。实验证明该方法的稳健性，达到了 MPIIGaze 和 EyeDiap 数据集上的最新成果，分别为 3.89 和 4.42。此外，通过使用生成模型生成的不同样式的面部图像进行进一步实验，证明了积极的推广效果。