Appearance debiased gaze estimation via stochastic subject-wise adversarial learning

Suneung Kim, Woo Jeoung Nam, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number110441
JournalPattern Recognition
Volume152
DOIs
Publication statusPublished - 2024 Aug

Bibliographical note

Publisher Copyright:
© 2024 Elsevier Ltd

Keywords

  • Adversarial loss
  • Appearance-based gaze estimation
  • Generalization
  • Meta-learning
  • Stochastic subject selection

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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