## 4 Experiments and Analysis of Results
### Reliability Validation of Finger Vein Image Damage Restoration Tasks  
To verify the reliability of the proposed damaged finger vein image inpainting method for finger vein image restoration in this paper, we designed the following experiment. To simulate the actual application situation, we divided the damaged areas of the images into \(20\times 20\), \(40\times 40\), \(60\times 60\), \(80\times 80\), \(100\times 100\), and \(120\times 120\) for testing, and divided the damaged areas into middle, left, and right. We calculated the EER values of the simulated damaged images in the public dataset by template matching with the images restored by the method proposed in this paper, as shown in Tables 7-9, respectively. The experimental results show that the recognition accuracy is low as the damaged area increases, leading to a lack of information on the texture details of the finger veins. Especially after the damaged area reaches \(80\times 80\), its EER value is significantly higher. Finger vein images restored by the method proposed in this paper showed smaller values of EER and a higher degree of matching performance compared to the unrepaired damaged images for template matching. The values of the EER for template matching of finger vein images after restoration by the method proposed in this paper were reduced by an average of 0.41% over the four datasets when the damaged area was between 20 and 80, compared to the images before restoration. When the damaged area is between 80 and 120, there is an average reduction of 1.71% compared to the unrestored images on the four datasets. It can be seen that the proposed method has better restoration performance and generalization ability for different damaged areas and damage sizes of the test dataset, especially for large damaged areas. To verify the effectiveness and reliability of the proposed method in the area of finger vein image damaged restoration, the finger vein images from the test datasets were processed by adding a random simulation of the damaged area and size, and the trained model was used for restoration. We used PSNR to quantitatively assess the image quality and image similarity of the images restored with the state-of-the-art method, as well as the method used in this paper to the original images, as shown in Table 10. It can be readily seen that the method used in this paper has a higher value of PSNR, indicating that the images after damaged restoration by the method used in this paper are of higher quality and have more vein detail information. At the same time, we plotted the ROC curves for the images recovered using the state-of-the-art method and the method used in this paper on each of the four public datasets, as well as for the original images, as shown in Figure 10. It can be found that the damaged finger vein images have low matching performance due to the lack of finger vein detail information. The restored finger vein images using the proposed inpainting method showed an improved performance in matching authentication compared to the unrestored images, with an average reduction in EER of 3.77% across the four datasets. This result is mainly due to the two-stage restoration process used in this paper, where the second stage is guided by the vein structure. The restored images have more visible vein details which show a lower ROC curve and lower values of EER compared to the state-of-the-art method, with an average reduction of 1.12% over the four datasets. To verify validity still further, we compared the network with only one-stage