# Gaze Estimation Based on a Multi-Stream Adaptive Feature Fusion Network
## 4 Experiments
### Performance
In particular, the complex lighting environment imposes higher robustness requirements on the model, facilitating a comprehensive evaluation of its adaptability in real-world applications. Therefore, training and testing on the EyeDiap dataset provide a more thorough verification of the proposed method's stability and performance advantages under complex conditions. As shown in Table 1, the proposed method achieves significant performance improvements on the EyeDiap dataset, demonstrating strong competitiveness. Specifically, compared to VGE-Net, the proposed method reduces the error by approximately 1.36\({}^{\circ}\), achieving a performance improvement of about 20%. As a baseline method, Dilated-Net, which uses left eye, right eye, and face images as input, has an error of 6.17\({}^{\circ}\) on the EyeDiap dataset, while the proposed method further reduces it by 0.93\({}^{\circ}\), highlighting its advan  
Figure 6: The baseline and FullFace, along with our model, were evaluated on the MPIIFaceGaze dataset using the leave-one-out validation strategy. tage among similar network architectures. Although there remains a performance gap between the proposed method and GazeCaps on the EyeDiap dataset, experiments on the MPIIFaceGaze dataset indicate that the proposed method excels in accuracy and maintains strong adaptability across different datasets. Overall, the method not only exhibits good generalization ability and stable gaze estimation performance but also maintains reliable estimation results even under complex lighting conditions. Additionally, compared to the recently proposed UniGaze-H method, our approach also demonstrates superior performance. To provide a more intuitive analysis of the proposed method's performance across different individuals, we adopt a leave-one-out validation strategy to ensure the reliability of the evaluation results. Figure 7 further illustrates the error distribution of the proposed method and baseline methods when trained separately for each individual. From the overall trend, the proposed method exhibits relatively small error fluctuations across different individuals, indicating strong stability. This suggests that the method is not only effective for specific individuals but also capable of achieving accurate gaze estimation at the group level. The Gaze360 dataset extends the research scope of the gaze estimation task by providing multi-view facial images over a 360\({}^{\circ}\) range, enabling the model to learn richer angular information, and thus improving its prediction ability under different viewpoints. Therefore, the experiments conducted on the Gaze360 dataset not only validate the prediction ability of the proposed method for small angle variations, but also examine its adaptability and generalization ability for a wide range of angle variations. The experimental results are shown in Table 1, where the proposed method achieves a performance improvement of 2.61\({}^{\circ}\) on the Gaze360 dataset compared to the baseline method, which significantly improves the accuracy of the multi-angle gaze estimation task. However, there is still a performance gap of 1.08\({}^{\circ}\) compared with the recently proposed GazeCaps method, which is optimized for hierarchical representation of gaze direction features and spatial relationship modeling, and shows stronger performance, especially in the large-angle gaze estimation task, which effectively captures the nonlinear changes in gaze direction with the help of capsule networks and exhibits stronger generalization ability. This gap mainly stems from the advantage of GazeCaps in processing 3D structural information. In comparison, the proposed method outperforms the UniGaze-H method by 1.25\({}^{\circ}\). Experimental results on Gaze360 show that the proposed method not only maintains the accurate prediction of small angles, but also significantly enhances the prediction of a wide range of angles.