ADGaze: Anisotropic Gaussian Label Distribution Learning for fine-grained gaze estimation

Abstract

Gaze estimation technology is crucial for enhancing the effectiveness and safety of applications in human–computer interaction, intelligent driving, virtual reality, and medical diagnosis. With advancements in deep learning, gaze estimation methods using deep neural networks have been extensively researched and applied. However, existing methods have yet to address the anisotropic characteristics of eye features. Based on the discovered anisotropic characteristics, we propose an Anisotropic Gaussian Label Distribution Learning Network for Gaze Estimation (ADGaze). ADGaze is capable of catching neighboring information by taking advantage of coarse-to-fine methodology and the anisotropic soft label construct. The coarse-to-fine framework initially performs classification tasks for gaze estimation, grouping gaze images with small variations into the same category, followed by regression tasks for each category. The construction of anisotropic Gaussian label distributions adopts methods based on data statistics and feature similarity. Extensive experimentation on public datasets has been carried out to substantiate the efficacy of this model. Our code is publicly available at https://github.com/dacilab/ADGaze.