# 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
Experiment 3: Performance Analysis of algorithms on Small Sample and Small Categories of Finger Vein Datasets  
Artificial intelligence technology, represented by deep learning, is booming, and there are more applications in the field of finger vein recognition. However, deep learning is more suitable for large datasets in applications. Deep learning needs to automatically learn features from the data, which is usually only possible with a large amount of training data. However, the model we proposed can perform better on the small amount of finger vein datasets. Our model could better explore the relationship between data features and maximize the performance of the model. In most cases, to improve the performance of deep learning-based finger vein recognition models, the finger vein datasets are augmented to obtain more experimental samples. We redesigned the experiment to test the model's performance on a finger vein dataset with fewer samples. We used a lesser number of images for training and compared the results, and we compared the proposed method with capsule networks. We redivided the four public datasets, and when the test set was determined to be one sample, the data of the training set was reduced in turn, and the division methods of 4-1 and 3-1 were selected, respectively, to test the effect of the model in the case of a small sample of data. Table 7 shows the comparison results of the proposed model ViT-Cap and capsule network in accuracy, EER and AUC values under the condition of reducing training samples. The experimental results showed that the performance of our model decreased to a certain extent when the sample size was reduced. However, compared with deep learning models, such as capsule networks, that process small sample data better, our model could still achieve better performance than the capsule network model with fewer samples, and the average recognition accuracy of our model was 91.7%. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Year** & **MMCBNU** & **SDUCLA** & **FV-USM** & **HKPU** \\ \hline [PERSON] et al. [17] & 2003 & 5.74 & 5.85 & - & 3.32 \\ [PERSON] et al. [19] & 2005 & 2.69 & 3.65 & - & 2.41 \\ [PERSON] et al. [37] & 2018 & - & - & 0.80 & 2.33 \\ [PERSON] et al. [38] & 2019 & & 1.69 & 0.94 & 2.40 \\ [PERSON] et al. [39] & 2021 & 1.68 & - & 2.12 & 4.23 \\ [PERSON] et al. [40] & 2021 & - & 2.23 & - & - \\ Proposed & 2021 & **0.63** & **1.3** & **0.28** & **1.66** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison in terms of EER (%). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Training** & **Testing** & **Model** & **ACC (\%)** & **EER (\%)** & **AUC** \\ \hline \multirow{4}{*}{MMCBNU} & 4 images & 1 image & Capsule & 90.83 & 1.87 & 0.99 \\  & 3 images & 1 image & ViT-Cap & 91.66 & 1.17 & 0.99 \\  & & & Capsule & 86.66 & 2.53 & 0.99 \\  & & & ViT-Cap & 87.16 & 2.49 & 0.99 \\ \hline \multirow{4}{*}{SDUMLA} & 4 images & 1 image & Capsule & 88.50 & 5.06 & 0.98 \\  & & & ViT-Cap & 90.25 & 3.11 & 0.99 \\  & 3 images & 1 image & Capsule & 81.13 & 8.02 & 0.97 \\  & & & ViT-Cap & 82.71 & 4.13 & 0.99 \\ \hline \multirow{4}{*}{FV-USM} & 4 images & 1 image & Capsule & 93.08 & 1.46 & 0.99 \\  & & & ViT-Cap & 94.31 & 2.21 & 0.99 \\  & 3 images & 1 image & Capsule & 88.33 & 4.52 & 0.99 \\  & & & ViT-Cap & 89.02 & 3.61 & 0.99 \\ \hline \multirow{4}{*}{HKPU} & 4 images & 1 image & Capsule & 89.04 & 4.24 & 0.98 \\  & & & ViT-Cap & 93.09 & 2.35 & 0.99 \\ \cline{1-1}  & & & Capsule & 82.38 & 8.94 & 0.96 \\ \cline{1-1}  & & & ViT-Cap & 92.14 & 2.31 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Finger vein recognition performance of ViT-Cap on small sample datasets.