FR-Net:A Light-weight FFT Residual Net For Gaze Estimation

TL;DR

FR-Net is a lightweight, FFT-based neural network for gaze estimation that achieves high accuracy with significantly fewer parameters and FLOPs, making it suitable for resource-constrained environments.

Contribution

The paper introduces FR-Net, a novel lightweight gaze estimation model that leverages FFT for feature extraction and a shortcut for improved accuracy, reducing computational costs.

Findings

Achieves lower gaze error angles (3.86 and 4.51) on MPII and EYEDIAP datasets.

Uses 17 times fewer parameters (0.67M) and 12% of FLOPs (0.22B).

Outperforms existing lightweight methods in accuracy and efficiency.

Abstract

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.