# Pupil Refinement Recognition Method Based on Deep Residual Network and Attention Mechanism
## 3 Experimental Analysis
The recognition rates of known and unknown class samples are as high as 95.0% and 88.0%, respectively, with an average recognition rate of 91.5% and a false recognition rate of only 5.0%. The recognition stability index is 9.0. This indicates that the method proposed in  
Figure 7: Fine pupil recognition effect of different methods. (**a**) Iris recognition method of ripple transformation. (**b**) CNN’s iris recognition method. (**c**) Iris recognition method for residual images. (**d**) Iris recognition method based on local gradient pattern. (**e**) Iris recognition method for Levenshtein distance. (**f**) Iris recognition method used in this paper. this article has significant advantages in the field of fine pupil recognition. In summary, compared with the other five iris recognition methods, this method demonstrates superior performance and stability in fine pupil recognition. This is mainly due to the optimization of feature extraction and classification strategies in our method, which results in a clear separation trend of recognition values between known and unknown class samples on the distribution map, thereby reducing the degree of confusion between categories and improving recognition accuracy and stability. Therefore, the method proposed in this article has broad application prospects in the field of fine pupil recognition. In order to verify the effectiveness of each module in the pupil refinement recognition method based on deep residual network and attention mechanism, the contribution of deep residual network ResNet101, channel attention module, external attention module, and Softmax classifier in pupil refinement recognition was verified by comparing the pupil refinement recognition effects under different module combinations. A dataset containing pupil images captured by infrared spectroscopy was used to ensure sufficient samples for reliable experiments. The same deep learning framework and hardware configuration were used to ensure the fairness of experimental results. Accuracy, recall, and F1 score were introduced as evaluation metrics to measure the effectiveness of pupil refinement recognition. The method proposed in this article was compared with pupil detection methods based on the Hough transform, edge detection, and machine learning algorithms. The specific experimental results are shown in Table 4.  
According to an analysis of Table 4, the proposed pupil refinement recognition method based on a deep residual network and attention mechanism shows significant advantages in accuracy, recall, and F1 score compared to traditional pupil detection methods based on Hough transform and edge detection and machine learning algorithms. The accuracy of this method is as high as 98.5%, the recall rate is 99.0%, and the F1 score is 98.8%, all of which are superior to other comparative methods. When removing the channel attention module or external attention module, although the recognition effect is still better than traditional methods, compared to the complete model, its accuracy, recall, and F1 score have all decreased, which verifies the important role of the channel attention module and external attention module in improving the fine pupil recognition effect. In summary, this method not only achieves high-precision pupil recognition but also proves the effectiveness of each module through ablation experiments, providing new ideas and technical support for the field of fine pupil recognition.