Appearance Debiased Gaze Estimation via Stochastic Subject-Wise
  Adversarial Learning
Suneung Kim Woo-Jeoung Nam Seong-Whan Lee
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.