# Gaze Estimation Based on a Multi-Stream Adaptive Feature Fusion Network
## 4 Experiments
### Ablation Study  
In this section, we conduct a series of ablation experiments to verify the effectiveness and advantages of each stream and module in our method. Our design employs a multi-stream and multi-input approach. For the gaze estimation task, we tested the performance of two independent streams and the improved module on the MPIIFaceGaze dataset and compared the results with existing techniques to confirm that the design concept of our model is both reasonable and effective. First, in the gaze estimation task, we conducted experiments using only the eye stream. Specifically, only the left and right eye images were used as inputs, and deep ConvNeXt modules were employed to extract deep features. The experimental results are shown in Table 3. Data analysis indicates that by stacking deep convolutional modules and utilizing a large receptive field, the eye stream demonstrates significant improvements in ocular feature extraction. However, compared to baseline methods and other advanced approaches, there is still room for further optimization. This suggests that deep network structures are indeed advantageous for ocular feature extraction but may also lead to overfitting issues, potentially affecting the model's optimal performance. Subsequently, we conducted experiments on the fusion stream, using both eye images and facial images as inputs. The ablation study results show that incorporating facial image features can significantly enhance the accuracy of gaze estimation. This experimental finding validates the importance of facial information in gaze estimation tasks. Ablation experiments were conducted to validate the contribution and effectiveness of each stream in the multi-stream, multi-input approach for gaze estimation. When using only the eye stream for gaze estimation, the error compared to MSMI-Net was 1.24\({}^{\circ}\). However, when employing the fusion stream, which incorporates both left and right eye images along with facial images, the error difference decreased to 0.47\({}^{\circ}\) compared to MSMI-Net. These results indicate that the multi-stream, multi-input strategy, which integrates high-dimensional eye features and low-dimensional combined eye-face features, significantly enhances gaze estimation accuracy. This approach effectively leverages key information from both the eye and facial regions while capturing subtle variations, thereby improving gaze direction prediction capability. Additionally, the multi-stream framework \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **MPIIFaceGaze** \\ \hline Without Fusion-Stream & 5.09 \\ Without Eye-Stream & 4.32 \\ MSMI-Net & 3.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation Experiments on Streams. Figure 9: Visualization results. The first row shows the input images, while the second and third rows display the ground truth and the estimated results from the proposed method, respectively. increases model flexibility, allowing it to dynamically adjust its decision-making process to handle gaze estimation tasks of varying difficulty. To evaluate the contribution of each module to the overall model performance, a series of detailed ablation experiments were conducted. The experimental results, as shown in Table 4, indicate that the model's performance significantly declined after removing the AWAM module. Specifically, when this strategy module was eliminated and feature fusion was directly performed using the JointCBAM module, the performance noticeably deteriorated, demonstrating the crucial role of AWAM in the feature fusion process. This mechanism dynamically adjusts the weights of different features, allowing the model to prioritize the most discriminative features during fusion. As a result, it effectively enhances the accuracy of feature extraction and fusion, providing a more precise feature representation and ultimately improving gaze estimation accuracy. Further ablation experiments revealed that the JointCBAM module plays a vital role in effectively integrating eye and facial features.