Appearance debiased gaze estimation via stochastic subject-wise adversarial learning

• Published in: Pattern recognition Vol. 152; p. 110441
• Main Authors: Kim, Suneung, Nam, Woo-Jeoung, Lee, Seong-Whan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2024
• Subjects: Adversarial loss; Appearance-based gaze estimation; Generalization; Meta-learning; Stochastic subject selection; Stochastic subject selection; Adversarial loss; Generalization; Appearance-based gaze estimation; Meta-learning
• DOI: 10.1016/j.patcog.2024.110441
• ISSN: 0031-3203

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 MPIIFaceGaze 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. •We suggest adversarial training for gaze estimation to generalize facial appearances.•We propose a stochastic strategy for subject-wise training to improve generalization.•Our framework achieves state-of-the-art performance on the MPIIFaceGaze and Eyediap.