## 2 Research on the Key Technology of Eye Control Methods
### Extraction and Recognition of Eye Movement Features  
Eye movement information is a key factor in human-computer interaction and can reveal the user's focus of attention when processing information [9]. In order to effectively identify the user's interaction intent, we used an improved YOLOv5 model to detect and classify eye movements. YOLOv5 is a single-shot multi-frame detector that is capable of processing images in real time and detecting multiple objects in the image. We collected six eye movement characteristics, including the duration of fixation, fixation frequency, fixation interval, eye movement velocity, jump amplitude, and pupil diameter.  
For classifying the fused features, a support vector machine (SVM) was selected due to its strong performance in handling high-dimensional feature spaces, which is essential for capturing and expressing complex user intentions. Compared to random forest (RF) and neural networks (NNs), an SVM demonstrates better generalization ability, particularly in small datasets. An SVM is able to segment data points into different categories by finding an optimal boundary in a high-dimensional space, making it very suitable for distinguishing between intentional and unintentional eye movement behavior [7]. While neural networks might perform well with large datasets, they carry a higher risk of overfitting and require extensive tuning and computational resources, making them less suitable for small datasets in this study. Random forest, while effective in certain classification tasks, may lead to increased complexity and computational costs when dealing with high-dimensional data. The experimental results showed that the SVM outperformed both RF and NN in terms of accuracy and F1 score, further validating its suitability and superiority in this study. The Eye movement intent detection flow chart is shown in Figure 3.  
In this experiment, 1059 sets of eye movement feature data were collected and divided into training data and test data according to a ratio of 8:2. In order to evaluate the effectiveness of the intended eye movement detection model based on the SVM, the nearest  
Figure 3: This eye movement intent detection flow chart describes the conversion of eye movement data to intent classification.  
neighbor algorithm (KNN) and random forest algorithm (RF) were used in this study to construct a binary classification model for eye movement recognition. In the test stage, by comparing the accuracy rate, accuracy rate, recall rate, and F1 score of the eye movement recognition model based on the SVM, RF and KNN algorithms on the constructed dataset, the experimental results, as shown in Table 1, show that the SVM classifier has a good classification performance, which can be used in natural visual behaviors. The user's intentional eye movements are detected by the selected eye movement features.  
To quantitatively assess the contribution of individual features, namely facial expressions and eye movements, to the performance of the overall intent recognition system, we conducted an ablation study. The study involved training the model for each feature type independently and evaluating its performance separately. The results of this study are detailed in Table 2 below.  
In order to improve the accuracy of intention recognition through feature fusion, we compared various fusion techniques, including self-attention, joining, and bilinear pooling. Self-attention methods outperformed other methods by enabling the model to focus dynamically on the most relevant features. The results of comparative analysis are presented in Table 3.