Gaze estimation, which predicts gaze direction, commonly faces the challenge of interference from complex gaze-irrelevant information in face images. In this work, we propose DMAGaze, a novel gaze estimation framework that exploits information from facial images in three aspects: gaze-relevant global features (disentangled from facial image), local eye features (extracted from cropped eye patch), and head pose estimation features, to improve overall performance. Firstly, we design a new continuous mask-based Disentangler to accurately disentangle gaze-relevant and gaze-irrelevant information in facial images by achieving the dual-branch disentanglement goal through separately reconstructing the eye and non-eye regions. Furthermore, we introduce a new cascaded attention module named Multi-Scale Global Local Attention Module (MS-GLAM). Through a customized cascaded attention structure, it effectively focuses on global and local information at multiple scales, further enhancing the information from the Disentangler. Finally, the global gaze-relevant features disentangled by the upper face branch, combined with head pose and local eye features, are passed through the detection head for high-precision gaze estimation. Our proposed DMAGaze has been extensively validated on two mainstream public datasets, achieving state-of-the-art performance.

本文提出了一种新颖的注视估计框架DMAGaze，旨在解决面部图像中与注视无关的复杂信息对注视估计的干扰。通过引入连续的基于掩码的解耦器和多尺度全局局部注意力模块，该方法有效地提取了与注视相关的全局特征和局部眼部特征，提高了注视估计的精确度。实验证明，该方法在两个主流公开数据集上达到了最先进的性能。

DMAGaze：基于特征解耦和多尺度注意力的注视估计