# Gaze Estimation Based on a Multi-Stream Adaptive Feature Fusion Network
## 4 Experiments
### Performance  
To evaluate the effectiveness of the proposed method, multiple methods were compared on three commonly used standard datasets: EyeDiap, MPIIFaceGaze, and Gaze360. Eleven state-of-the-art methods were selected as a control group, including AFF-Net [44], Dilated-Net [41], CA-Net [33], VGE-Net [45], FullFace [19], MSGazeNet [46], RT-GENE [47], ARE-Net [20], GazeCaps [48], UniGaze-H [49], and GazeSymCAT [50], and in order to validate the present chapter more comprehensively method performance and validity, Dilated-Net was selected as the experimental baseline in this study. The experimental results are shown in Table 1, demonstrating the advantages of the proposed method over other methods in recent years. In the performance on the three datasets, the proposed method achieves a significant improvement of 0.59\({}^{\circ}\), 0.93\({}^{\circ}\), and 2.61\({}^{\circ}\) in terms of error compared to Dilated-Net, respectively. This indicates that the proposed network not only retains the advantage of handling small angular variations but also significantly improves the accuracy of large head angle prediction. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MPIIFaceGaze** & **EyeDiap** & **Gaze360** \\ \hline AFF-Net & 4.34 & 6.41 & - \\ Dilated-Net & 4.44 & 6.17 & 13.73 \\ CA-Net & 4.27 & 5.27 & 11.20 \\ RT-GENE & 4.66 & 6.02 & 12.26 \\ FullFace & 4.93 & 6.53 & 14.90 \\ ARE-Net & 5.00 & 6.1 & - \\ MSGazeNet & 4.64 & 5.86 & - \\ VGE-Net & 3.90 & 6.6 & - \\ GazeCaps & 4.06 & **5.10** & **10.04** \\ UniGaze-H & 4.51 & 5.88 & 12.37 \\ GazeSymCAT & 4.11 & 5.13 & - \\ MSMI-Net & **3.85** & 5.24 & 11.12 \\ \hline \hline \end{tabular} Bold text indicates the best performance among the current comparison methods.  
\end{table} 
Table 1: Experimental errors of related methods. We conduct experimental validation on the MPIIFaceGaze, EyeDiap, and Gaze360 datasets. Dilated-Net is selected as the baseline. The MPIIFaceGaze dataset is a recognized benchmark in the field of 3D gaze estimation. Compared to the MPIIGaze dataset, it includes full-face images, making it more suitable for studying the relationship between facial features and eye gaze direction. Additionally, the dataset has a smaller range of gaze angle variations, making it effective for evaluating model performance under subtle gaze changes. Experimental results show that the proposed method outperforms the earlier FullFace method on the MPIIFaceGaze dataset, reducing error by 1.08\({}^{\circ}\) and improving performance by 21%, which strongly demonstrates its advantages in gaze estimation tasks. Although the VGE-Net method has not been tested on the MPIIFaceGaze dataset, it achieved an accuracy of 3.9\({}^{\circ}\) on the MPIIGaze dataset, surpassing other methods and exhibiting strong performance. Further comparative analysis indicates that the proposed method achieves the best results on the MPIIFaceGaze dataset compared to GazeSymCAT and UniGaze-H, demonstrating strong generalization ability and robustness. Given the particularity of the MPIIFaceGaze dataset, this experiment employs a leave-one-out validation strategy to ensure a thorough evaluation of the model's generalization ability across different subjects. Additionally, detailed statistics and analyses of training errors for each participant were conducted to explore the model's adaptability and potential for optimization across individuals. The comparison results with baseline models can be found in Figure 6. Overall, the experimental results indicate that the proposed method has significant advantages in gaze estimation tasks, maintaining stable performance across different individuals while exhibiting high robustness and generalization ability. To further enhance the model's performance, this study conducts an in-depth training and comparative analysis on the EyeDiap dataset. This dataset encompasses various complex scenarios, including different head movement patterns, gaze deviations, and varying lighting conditions, making it a highly challenging testing platform.