# Pupil Refinement Recognition Method Based on Deep Residual Network and Attention Mechanism
## 3 Experimental Analysis
In this pupil dataset, 10 individuals' pupil images were randomly selected. Although the number of these 10 individuals is limited, their pupil characteristics cover different ranges, including pupil center position, diameter, and shape parameters (roundness). Therefore, from preliminary observations, these 10 individuals have a certain representativeness in terms of pupil characteristics. Using the method described in this article, fine pupil recognition was performed on these 10 individuals, and the results of the fine pupil recognition are shown in Table 2.  
Analysis of Table 2 shows that the method in this paper can effectively complete the pupil refinement recognition and obtain the pupil's center position, pupil diameter, and pupil shape parameters--three aspects of the refinement of the recognition results that provide data support for identity authentication, emotion analysis, disease diagnosis, human-computer interaction, and other fields. Taking the iris recognition method using ripple transform in [6], the iris recognition method using CNN in [7], the iris recognition method using the residual image in [8], the iris recognition method using local gradient pattern in [9], and the iris recognition method using Levenshtein distance in [10] and comparing them with the methods in this paper, the pupil refinement recognition effects of these six methods were analyzed in order to visually display the pupil refinement recognition performance of each method and draw their recognition results distribution maps on the verification set. The rendering process is as follows: First, the pupil refinement recognition results of the validation set samples are obtained. Then, the recognition value used to determine the category in the recognition result is extracted according to the category discrimination benchmark; at this time, each tested sample only corresponds to one recognition value. Finally, the number of the extracted recognition values in each threshold interval is determined, and a histogram of the statistical results is drawn, as shown in Figure 7. In the figure, the green stripe represents the statistics of the recognition results of known samples, and the pink stripe represents the statistics of the recognition results of unknown samples. The detailed data are shown in Table 3.  
Figure 6: t-SNE visualization. From the analysis of Figure 7 and Table 3, it can be seen that the iris recognition method using ripple transform extracts iris features. Although it achieves certain recognition effects, the recognition rates of known and unknown class samples are relatively low, at 85.0% and 72.0%, respectively, with an average recognition rate of 78.5% and a high false recognition rate of 15.0%. This indicates that the ripple transform has insufficient robustness in feature extraction when dealing with complex or highly variable iris images. Compared with the method proposed in this article, the recognition performance of the ripple transform method is significantly lower, especially in the recognition of unknown class samples, with a large gap. At the same time, the false recognition rate and recognition stability are also poor. The CNN iris recognition method uses deep convolutional networks to automatically learn iris features, significantly improving recognition performance. The recognition rates of known and unknown class samples reached 90.0% and 80.0%, respectively, with an average recognition rate of 85.0% and a false recognition rate of 10.0%. This indicates that CNN has advantages in handling large-scale datasets and extracting complex features. Although the CNN method performs well, compared with the method proposed in this paper, there is still room for improvement in the recognition of unknown class samples, and the recognition stability and average recognition rate are slightly lower. The iris recognition method of residual images enhances iris features by constructing residual images, thereby improving recognition accuracy.