Gaze estimation is a crucial task in computer vision, however, existing methods suffer from high computational costs, which limit their practical deployment in resource-limited environments. In this paper, we propose a novel lightweight model, FR-Net, for accurate gaze angle estimation while significantly reducing computational complexity. FR-Net utilizes the Fast Fourier Transform (FFT) to extract gaze-relevant features in frequency domains while reducing the number of parameters. Additionally, we introduce a shortcut component that focuses on the spatial domain to further improve the accuracy of our model. Our experimental results demonstrate that our approach achieves substantially lower gaze error angles (3.86 on MPII and 4.51 on EYEDIAP) compared to state-of-the-art gaze estimation methods, while utilizing 17 times fewer parameters (0.67M) and only 12\% of FLOPs (0.22B). Furthermore, our method outperforms existing lightweight methods in terms of accuracy and efficiency for the gaze estimation task. These results suggest that our proposed approach has significant potential applications in areas such as human-computer interaction and driver assistance systems.

文章提出了一种名为 FR-Net 的轻量级模型，利用快速傅里叶变换（FFT）从频域提取注视相关特征并减少参数数量，同时引入空间域的快捷方式组件进一步提高了模型的准确性，实验结果表明，相较于现有方法，该方法具有更高的准确性和效率，并适用于人机交互和驾驶员辅助系统等领域。

FR-Net: 一种轻量级FFT残差网络用于凝视估计