## 4 Experiments and Analysis of Results
### Reliability Validation of Finger Vein Image Damage Restoration Tasks
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{FV-USM} & \multicolumn{4}{c}{UTFVP} \\ \cline{2-9}  & \multicolumn{2}{c}{\(\sigma=16\)} & \multicolumn{2}{c}{\(\sigma=20\)} & \multicolumn{2}{c}{\(\sigma=16\)} & \multicolumn{2}{c}{\(\sigma=20\)} \\ \cline{2-9}
**Blur** & & & & & & & & \\
**Conv** & **Blurred** & **Restored** & **Blurred** & **Restored** & **Blurred** & **Restored** & **Blurred** & **Restored** \\
**Kernel** & & & & & & & & \\ \hline \(10\times 10\) & 6.50 & **3.79** & 6.36 & **3.86** & 16.80 & **11.48** & 17.52 & **12.01** \\ \(15\times 15\) & 8.12 & **5.35** & 8.13 & **5.55** & 21.56 & **15.32** & 22.43 & **15.41** \\ \(20\times 20\) & 10.22 & **7.02** & 10.36 & **7.81** & 26.31 & **17.15** & 26.59 & **18.09** \\ \(25\times 25\) & 12.05 & **9.31** & 12.24 & **9.72** & 29.09 & **18.54** & 30.11 & **19.52** \\ \(30\times 30\) & 12.77 & **10.67** & 13.18 & **11.33** & 32.21 & **22.15** & 25.43 & **19.91** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: EERs (%) of FV-USM and UTFVP with different \(\sigma\) and blur Conv kernels. restoration with the two-stage restoration proposed in this paper. On the four datasets, the EER of the two-stage restoration proposed in this paper is on average 1.33% lower than that of the one-stage restoration. This is a sound and reliable indication of the effectiveness of the two-stage restoration as proposed in this paper. In order to verify that the images restored by this paper are rich in feature information, we did experiments based on feature point matching on four publicly available datasets, and plotted the intra-class and inter-class probability density distribution plots and feature point histogram statistical plots of different algorithms to analyze the reliability of the algorithms in this paper, as shown in Figures 11 and 12. It can be seen that after the defective images are repaired, the probability density curve of feature points in the in-class images is more to the right, and the histogram statistics of feature points are more to the right and more in number; the probability density curve of feature points in the out-class repaired images is more to the zero point, and the histogram statistics of feature points are more to the left and less in number, which indicates that the defective images can be repaired by this paper with more details than other defective repair methods. The results show that more detailed information can be recovered by this method than by other defect restoration methods. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**MMCBNU\_6000**} & \multicolumn{2}{c}{**SDUMA-FV**} & \multicolumn{2}{c}{**FV-USM**} & \multicolumn{2}{c}{**UTFVP**} \\ \cline{2-10} \multicolumn{1}{c}{**The Size**} & & & & & & & & \\ \multicolumn{1}{c}{**of**} & & & & & & & & \\ \multicolumn{1}{c}{**Damaged**} & & & & & & & & \\ \multicolumn{1}{c}{**Area**} & & & & & & & & \\ \hline \(20\times 20\) & 6.85 & 6.55 & 6.05 & 5.63 & 2.45 & **2.43** & 6.59 & **6.11** \\ \(40\times 40\) & 7.10 & **6.69** & 6.34 & 5.93 & 2.84 & **2.50** & 6.94 & 6.59 \\ \(60\times 60\) & 7.63 & **6.81** & 7.28 & **6.57** & 3.11 & **2.82** & 8.24 & **6.84** \\ \(80\times 80\) & 7.68 & **7.43** & 7.96 & **7.37** & 3.64 & **3.25** & 8.54 & 7.09 \\ \(100\times 100\) & 8.57 & **7.45** & 8.43 & **7.74** & 3.94 & **3.39** & 10.69 & **7.22** \\ \(120\times 120\) & 9.42 & **7.98** & 9.32 & **8.08** & 4.26 & **3.55** & 10.87 & **7.89** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: EERs (%) of the left damaged area on the different public datasets.