  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.
