# ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition
## 4 Results
### Experiment 2: Comparison with State-of-the-Art Finger Vein Recognition Algorithms  
In the second set of experiments, we selected four state-of-the-art methods to conduct experiments on four publicly available datasets. The first algorithm we chose was the vision transformer model, and we applied it to finger vein recognition. The second algorithm we chose was the capsule network model, which used the capsule network to train finger vein images, and the third algorithm selected was the finger vein recognition algorithm based on convolutional neural network proposed by [PERSON] et al. [14], which obtained stable and high-precision performance when processing finger vein images of different qualities. The last algorithm we chose was the method based on maximum curvature proposed by [PERSON] et al. [23]. The algorithm extracted the features used for finger vein recognition by calculating the local maximum curvature point of the finger vein cross-section, and it became a presentative algorithm in the field of finger vein recognition. Table 5 shows the recognition accuracy achieved by four comparative finger vein recognition algorithms and ViT-Cap algorithm. Experimental results indicated that the recognition performance of the proposed method was better than that of other methods. As can be seen from the obtained recognition accuracy, the average recognition accuracy of our proposed model on four public datasets was 96.26%, which was significantly better than other advanced algorithms. The recognition accuracy of ViT-Cap model on FV-USM, SDUMLA, MMCBNU, and HKPU datasets was 98.68%, 93.24%, 97.52%, and 95.61%, respectively, while the average accuracy of the vision transformer and capsule network model was only 92.41% and 95.87%, respectively. This indicated that our proposed model has excellent extraction ability of finger vein features. Compared with the existing methods, the ViT-Cap provided a new reference model for finger vein recognition. EER plays an important role in evaluating the recognition effect of finger vein models, so we selected the recognition algorithms proposed in six cutting-edge papers on finger vein recognition and experimented with our algorithm and compared the obtained EER results. Table 6 shows the EER values obtained by our model and the EER results of other related algorithms. Repeated line tracking algorithm and maximum curvature algorithm were representative finger vein recognition methods, but all had high EER values in three public datasets. We also compared our model with the deep learning-based recognition algorithms proposed in recent years [37; 38; 39; 40] and found that our model still achieved the best results. The results of Table 6 are the results of a direct citation of the original paper. We ensured that we compared each method based on the same datasets and metrics and used the same number of training and testing datasets. For each, the best results were selected for comparison. Therefore, we believe that the comparison was fair, and our model can withstand the test of different methods. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **ACC (\%)** \\ \hline Resnet50 + CapsNet & 93.13 \\ MobilenetV3 + CapsNet & 89.07 \\ Proposed & 95.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation study on MIXFV. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Training**} & \multirow{2}{*}{**Testing**} & \multicolumn{6}{c}{**Accuracy**} \\ \cline{4-8}  & & & & **ViT** & **Capsule Net** & **CNN [9]** & **MC [19]** & **ViT-Cap** \\ \hline MMCBNU & 6 images & remaining 4 images & 95.13 & 96.29 & - & - & **97.52** \\ SDUMLA & 5 images & remaining 1 image & 90.88 & 95.66 & 97.48 & **97.95** & 93.24 \\ FV-USM & 8 images & remaining 4 images & 95.99 & 96.47 & 97.53 & 90.34 & **98.68** \\ HKPU & 8 images & remaining 4 images & 87.62 & 95.07 & 95.13 & 85.24 & **95.61** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison with state-of-the-art methods of finger vein recognition.