# ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition
## 4 Results
### Experiment 1: Preliminary Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Database**} & **Number** & \multirow{2}{*}{**Heads**} & **Routing** & \multirow{2}{*}{**ACC (\%)**} & \multirow{2}{*}{**EER (\%)**} & \multirow{2}{*}{**AUC**} \\  & **of Layers** & & & **Iterations** & & \\ \hline \multirow{4}{*}{MMCBNU} & 1 & 12 & 3 & 96.54 & 0.66 & 1.0 \\  & 1 & 24 & & 97.52 & 0.63 & 1.0 \\  & 2 & 12 & 4 & 95.29 & 1.12 & 1.0 \\  & 2 & 24 & 4 & 97.25 & 0.65 & 1 \\ \hline \multirow{4}{*}{SDUMA} & 1 & 12 & 3 & 91.98 & 2.15 & 0.99 \\  & 2 & 24 & & 94.97 & 1.13 & 1 \\  & 2 & 12 & 4 & 92.45 & 2.38 & 0.99 \\  & 2 & 24 & 4 & 93.24 & 1.74 & 0.99 \\ \hline \multirow{4}{*}{FV-USM} & 1 & 12 & 3 & 97.76 & 1.01 & 1.0 \\  & 2 & 24 & 3 & 98.68 & 0.29 & 1.0 \\  & 2 & 12 & 4 & 97.82 & 0.28 & 1.0 \\  & 2 & 24 & 4 & 97.92 & 0.60 & 1.0 \\ \hline \multirow{4}{*}{HKPU} & 1 & 12 & 3 & 92.62 & 3.53 & 0.99 \\  & 2 & 24 & 3 & 93.45 & 2.93 & 0.99 \\ \cline{1-1}  & 2 & 12 & 4 & 95.36 & 2.56 & 0.99 \\ \cline{1-1}  & 2 & 24 & 4 & 95.61 & 1.66 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of ViT-Cap on four datasets and under different parameter settings. Figure 7 shows the ROC curve obtained by the experiment. For the MMCBNU dataset, continuing to increase the depth of the network model degraded the performance of the model as the depth of the network model increased to a certain level. As can be seen from Figure 7a, increasing the number of layers of the encoder significantly reduced the performance of the network model without changing the number of heads. On the contrary, if we increased the number of heads, the performance of the model was further improved. Since the multi-head attention mechanism acquired long-range contextual information in the image without considering distance, it was possible to obtain the relationship between complex high-level semantic information in finger vein images thereby improving the accuracy of recognition. The ROC curve obtained after training the model with FV-USM dataset is shown in Figure 7b. The optimal model configuration was an encoder layer with 24 heads, and the number of routing iterations is set to 3. Furthermore, when the experimental results reached the upper limit of the model performance, continuing to change the configuration of the model did not improve the experimental results. We designed two sets of ablation experiments. We used Resnet50 and MobilenetV3 as the backbone to test performance. The experimental results are shown in Table 4. The experimental results showed that the two sets of ablation experiments achieved 93.13% and 89.07% of the accuracy, respectively, and the accuracy of our proposed model is 95.23%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Database** & \
\begin{tabular}{c} **Number** \\ **of Layers** \\ \end{tabular} & **Heads** &
\begin{tabular}{c} **Routing** \\ **Iterations** \\ \end{tabular} & **ACC (\%)** & **EER (\%)** & **AUC** \\ \hline MMCBNU & 12 & 24 & 6 & 96.08 & 0.72 & 1 \\ SDUMLA & 12 & 24 & 6 & 92.14 & 1.63 & 1 \\ FV-USM & 12 & 24 & 6 & 98.35 & 0.55 & 1 \\ HKPU & 12 & 24 & 6 & 95.48 & 1.71 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of ViT-Cap with deep layers and routing iterations on four datasets. Figure 7: ROC curves of the ViT-Cap model on four public finger vein datasets:(**a**) MMCBNU (**b**) FV-USM (**c**) HKPU (**d**) SDUMLA.