# An Iris Image Super-Resolution Model Based on Swin Transformer and Generative Adversarial Network
## 4 Experiments and Results
### Results
The final stage of iris recognition is to compare the iris code with the iris code in the iris database to determine if the recognition can be successful or not; thus, we think it is also necessary to compare the iris code of iris images generated by different methods. We use the open-source OSIRIS [20] to acquire the iris code for different methods of generating images. The specific results are shown in the Figure 14.  
The iris code has more information, and in order to show some of the details, we intercepted and zoomed in on the same area (red area) of the iris image generated by each of the different methods, as shown in Figure 15. It can be clearly seen that the iris code generated by our method is the closest to HR's iris code, so our method has a better recognition performance. The current mainstream metrics for measuring super-resolution reconstruction are not a good measure concerning the recognition performance of iris images, as indicated by the results in Table 2.  
As shown in Table 2, it can be observed that the super-resolution images are superior to the low-resolution images in all metrics of generated image quality. All the super-resolution \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **LR** & **SRCNN** & **ESRGAN** & **SwinIR** & **HAT** & **IrisDnet** & **SwinIris** & **SwinGIris** \\ \hline EER(VGG) & 2.9474 & 2.4737 & 1.7895 & 1.3158 & 1.0526 & 1.1053 & 1.1632 & **0.8421** \\ EER(ResNet) & 2.0526 & 0.8947 & 0.5263 & 0.4737 & 0.4211 & 0.4211 & 0.4737 & **0.2632** \\ EER(DenseNet) & 1.2632 & 0.6842 & 0.3684 & 0.4210 & 0.2105 & 0.2105 & 0.4211 & **0.1579** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of iris verification on CASIA-Iris-Interval (best results are indicated with bold). Figure 14: Visual results of iris code for CASIA-Iris-Interval dataset. Figure 15: Visual details of iris code for CASIA-Iris-Interval dataset. The red frame area is the hand details. models perform better than the low-resolution images regarding iris recognition performance, which demonstrates the effectiveness of super-resolution models in iris recognition. The experimental results show that all models achieve their lowest EER when using DenseNet121 as the backbone classification network, as the structure of dense connections has stronger classification capabilities. In contrast, as VGG16 has a simple structure, its recognition performance is worse. In comparison with other models, our model achieved the lowest EER on different networks due to the fact that the global information is taken into consideration during the design of the model, resulting in better structural information in the generated images. The incorporation of the GAN's architecture enhances the capacity to represent texture details, both of which are formally essential for the iris recognition process. The EER curves do not show significant variations. To effectively illustrate the trend of the EER curves, a logarithmic scale was employed for the X-axis, and the Y-axis was represented using decimal coordinates. As shown in Figure 16, it will be closed with only minor differences for networks featuring strong classification ability. As the iris information in this database is more abundant, mainstream super-resolution models exhibit similar performances. The model proposed in this paper reaches the best performance in most cases. The CASIA-Iris-Thousand dataset was employed to represent a multiclass dataset with unclear texture details collected in dim environments. The results of the comparison experiments are shown in Table 3.  
Compared to the CASIA-Iris-Interval dataset, more low-frequency information (e.g., eyelids, etc.) is present. In this case, as shown in Table 3, improved PSNR and SSIM metrics of the generated images are achieved when compared with the super-resolution model. HAT still performs the best in PSNR, and SSIM metrics. Here, it should be noted that SwinGIris obtains the highest score in PI. The overall results are similar to the results of the CASIA-Iris-Interval dataset.