GAFUSE-Net: A Low Resolution Gaze Estimation Method Based on the Enhancement of Gaze-Relevant Features

Abstract

Gaze estimation significantly enhances user interfaces, road safety, accessibility for the disabled, and consumer behavior analysis. Traditional methods depend on high-resolution images and ideal conditions, neglecting low-resolution scenarios. We propose GAFUSE-Net, designed for low-resolution gaze estimation. GAFUSE-Net includes a novel Facial Attention-Enhanced feature extraction (FAE) module and a Local image Super-resolution based Eye feature extraction (LSE) module for low resolution gaze estimation. The FAE module extracts gaze-relevant features from facial images. The LSE module uses super-resolution to enhance low-resolution eye images, followed by feature extraction with DeepEyeNet. Combined facial and eye features enable accurate gaze estimation. Experiments on the MPIIFaceGaze dataset show GAFUSE-Net’s effectiveness, achieving mean angular errors of 4.37°, 4.62°, 4.84°, and 6.19° at resolutions of $128\times 128$ , $64\times 64$ , $32\times 32$ , and $16\times 16$ , respectively. These errors are 0.2%, 1.3%, 5.7%, and 10.2% lower than state-of-the-art methods. The experimental results highlight the efficiency of GAFUSE-Net across various resolutions, significantly advancing the practical implementation of gaze estimation.

Keywords

• Gaze estimation
• low resolution
• feature fusion
• super-resolution
• gaze-relevant