# An Iris Image Super-Resolution Model Based on Swin Transformer and Generative Adversarial Network
## 4 Experiments and Results
### Results
Figure 17 shows the visual results of CASIA-Iris-Thousand dataset, in order to show the details of the generated images, we intercepted the same area (red area) of the iris images generated by different methods and enlarged them, as shown in Figure 18. As this dataset does not contain much iris information, the overall recovery of all methods is not as good as the CASIA-Iris-Interval dataset; however, our method is still visually the closest to the HR. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Bicubic** & **SRCNN** & **ESRGAN** & **SwinIR** & **HAT** & **IrisDnet** & **SwinIris** & **SwinGIris** \\ \hline PSNR & 33.4895 & 34.4581 & 34.8884 & 36.0513 & **36.3218** & 34.1663 & 35.8515 & 34.7214 \\ SSIM & 0.8339 & 0.8563 & 0.8905 & 0.9036 & **0.9152** & 0.8629 & 0.9011 & 0.8883 \\ PI & 8.2088 & 8.1540 & 6.3844 & 6.9119 & 7.6180 & 6.2439 & 6.8485 & **6.1892** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of image quality on CASIA-Iris-Thousand (best results are indicated with bold). Figure 16: DET curves for different classification networks of the CASIA-Iris-Interval dataset. Figure 19 shows visual results of the iris code for the CASIA-Iris-Thousand dataset, and Figure 20 provides visual details of the iris code for CASIA-Iris-Thousand dataset. Due to the limited information content in the CASIA-Iris-Thousand dataset, there are discernible differences between the iris codes recovered by all methods and the high-resolution (HR) reference iris codes. Consequently, the overall recognition performance of this dataset is not as robust as that on the CASIA-Iris-Interval dataset. However, it is noteworthy that our proposed method consistently generates iris codes that closely approximate the HR reference iris codes. As displayed in Table 4, the iris recognition performance is degraded due to the poor clarity of the iris texture in this dataset, while our model still achieves the best performance with classification networks of different capabilities. In contrast, the HAT performs poorly. This phenomenon may be caused by the interference resulting from the high amount of non-iris information (e.g., eyelids, etc.) in the dataset. The HAT is unable to fully focus on the recovery of the iris region. In contrast to SwinIris and SwinGIris, the improvement of iris recognition performance by sub-pixel convolutional progressive upsampling is more obvious than that of the CASIA-Iris-Interval dataset, which suggests that sub-pixel convolutional progressive upsampling is beneficial for focusing on the recovery of iris regions. This also highlights the stability of our method for different iris datasets. Figure 17: Visual results of CASIA-Iris-Thousand dataset. Figure 18: Visual details of CASIA-Iris-Thousand dataset. The red frame area is the hand details. In Figure 21, the DET curves for various models in the CASIA-Iris-Thousand dataset are presented. Compared to its performance on the CASIA-Iris-Interval, the advantage of our model is more noticeable in the CASIA-Iris-Thousand dataset. It is shown that our model can focus on the recovery of iris regions in the presence of more information interference. \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) & 4.8600 & 3.4806 & 2.5119 & 2.6761 & 3.4007 & 2.3200 & 2.5143 & **2.0995** \\ EER(ResNet) & 3.3600 & 1.9818 & 1.4101 & 1.3997 & 4.5828 & 1.3088 & 1.3595 & **1.1200** \\ EER(DenseNet) & 2.1200 & 1.2952 & 0.8583 & 0.9402 & 0.9001 & 0.7203 & 0.8199 & **0.6000** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of iris verification on CASIA-Iris-Thousand (best results are indicated with bold). Figure 21: DET curves for different classification networks of the CASIA-Iris-Thousand dataset. Figure 19: Visual results of iris code for CASIA-Iris-Thousand dataset. Figure 20: Visual details of iris code for CASIA-Iris-Thousand dataset. The red frame area is the hand details.