## 4 Experiments and Analysis of Results
### Reliability Verification of Motion Blur Restoration Tasks  
To verify the reliability of the proposed de-motion blurring restoration method for finger vein image restoration, several experiments were designed. In a practical application, the subject is captured with the finger vein image by the capture device. The system creates a template of the finger vein image and stores it in the database. When the subject needs to verify the identity of a template, the capture device recaptures the current finger vein image and matches it to the template in the database to obtain a match score. However, when creating the templates, the captured finger vein images may have motion blur due to external factors and need to be restored and then created into templates. Therefore, due to the different templates, we considered the following cases, which are close to the actual application. Case 1: The captured finger vein image is image restored by the method of this paper and then made into a template, and the template is matched with the finger vein image with motion blur during validation. Case 2: The captured finger vein image is restored by the method in this paper and then made into a template, and the template is matched with the restored finger vein image by the method in this paper. Case 3: The captured finger vein image from the acquisition device is made into a template directly without any processing, and the template is matched with the finger vein image with motion blur during verification. Case 4: The finger vein image captured by the capture device is made into a template directly without any processing, and the template is matched to the restored finger vein images during validation. Tables 2 and 3 show the values of EER obtained by doing matching authentication calculations on the public dataset for these four cases on different motion blur convolutions. Through the display of the experimental results in these tables, it can be found that as the convolution kernel becomes larger, the textured areas of the finger veins become more affected by the blurring, resulting in a serious lack of vein texture information and finally the performance of the recognition degrades. In Case 3 and Case 4, it is easy to find that the images captured by the acquisition device have motion blur, and after making the templates the feature information of the templates is not obvious due to the blurring, resulting in larger EER and lower recognition performance. Notably, the results of Case 4 were better than those of Case 3 due to the deblurring process of the matched images by the method in this paper. In Case 1 and Case 2, the captured images are deblurred by the method in this paper and then made into templates. The feature information of the templates was restored and the EER values calculated were lower and the recognition performance was better compared to Case 3 and Case 4. In Case 2, the matched data are deblurred compared to Case 1, which results in a better performance of Case 2. It is not difficult to see that, when comparing all the cases, only the template image and the matched image, which are both deblurred by the method in this paper, have smaller EER, higher image quality and better recognition performance in Case 2. Moreover, the generalization capability of our model can be seen by processing different motion blur convolution kernels. To verify the effectiveness and reliability of the method proposed in this paper in the field of finger vein image deblurring, we processed the finger vein images from the test datasets by adding random motion blur kernels and restored them with the trained model.