## 2 Research on the Key Technology of Eye Control Methods
### Interactive Intent Recognition Feature Fusion  
Feature Fusion Strategy Based on Attention Mechanism  
The attention mechanism fusion framework constructed in this paper is shown in Figure 4. Based on the two features extracted above, fusion was obtained after passing through an attention layer, and the support vector machine was used to classify the fused features to obtain the final intent recognition result. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline SVM & 90.8\% & 91.6\% & 92.7\% & 94\% \\ RF & 89.6\% & 88.4\% & 91.6\% & 91\% \\ KNN & 90.1\% & 87.3\% & 93.6\% & 89\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy, precision, recall, and F1 values for SVM, RF, and KNN algorithms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Type** & **Accuracy Rate** & **Precision** & **Recall Rate** & **F1 Score** \\ \hline Facial expression & 87.6\% & 89.1\% & 92.6\% & 90.6\% \\ Eye movement & 90.8\% & 91.6\% & 93.7\% & 92.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Individual feature performance comparison. Figure 4: Integration framework based on attention mechanism. The extracted facial expression features (marked as) and six kinds of eye movement features (marked as) judged with or without intention by the SVM were input into the classification network for feature fusion. Feature splicing is a standard feature-level fusion method widely used by many researchers. In this method, single features are connected in series to form new features, that is, the fused features are sent into the classification network for classification. For the two features extracted in this paper, a self-attention fusion strategy was used to calculate the fused features, so that the information between different features could focus on each other. For the self-attention method, the linear projection operation was first carried out to map these features to the same vector space, and then the mapped features were spliced together, that is, the spliced features were projected into a new vector space through a multi-head bidirectional projection, expressed as the dimension of each projection space, and i = [1, 2] was the index of two different features. After two projection transformations of the features, the self-attention mechanism was used to explore the complementary relationship between the features and learn the common features between different features. In multimodal fusion, eye movement features contribute more to accuracy compared to facial expression features, which can be explained from physiological and behavioral perspectives. Eye movements are a direct manifestation of human attention, closely linked to the user's gaze and information processing, making them more accurate in reflecting user intent. In contrast, facial expressions may be influenced by various factors such as emotional changes, lighting, and facial angles, which can lead to more indirect representations of user intent and potential recognition errors. Additionally, when facial expressions are subtle or less apparent, the system may struggle to accurately recognize user intent. By emphasizing the different roles of eye movements and facial expressions in intent recognition, the effectiveness of the multimodal fusion strategy can be better understood. Using the fused features from the attention mechanism, the following is obtained:  
\[\left\{ \begin{aligned} & A_{i}=softmax\bigg{(}\frac{{{z_{1}^{2}} \left({{z_{1}^{2}}}\right)^{T}}}{\sqrt{{{\rm{D}}_{s}}}}\bigg{)}{{Z_{i}^{3}}} \\ &\alpha_{i}=A_{i}\eta_{i}\\ &\vartheta_{i}=\sum\limits_{k=1}^{3}{{\alpha_{i}}[k]{A_{i}}[k,: ]}\end{aligned} \right. \tag{2}\]  
where \(\text{q}_{i}\in D_{s}\) is the learning parameter of each feature; is the third linear projection; all the projected features are spliced to obtain the fused feature, which is represented as \(\text{x}\,f\,usion\) = [\(\vartheta_{1}\), \(\vartheta_{2}\),..., \(\vartheta_{p}\)]; and p is the number of applied projections.