# Person Identification and Gender Classification Based on Vision Transformers for Periocular Images
## 4 Results
### Experiments for Gender Classification  
A separate classifier with two classes has been implemented through ViT for gender classification. Hence, we divided the dataset into two classes male and female. The baseline structure was trained with a 5-fold cross-validation strategy, obtaining the maximum accuracy of 99.04%. We further experimented to improve this result by modifying the original ViT architecture. The training was iterated again, obtaining the results with a maximum accuracy of 99.13%, which is indicated in Figure 8b. All experimental results pertaining to five ViT models for gender classification are depicted in Figure 8. It may be noted that for the experiments using the GC-ViT-B/16, GC-ViT-S/16, GC-ViT-B/32, and GC-ViT-S/32 architectures, the improved models (after inclusion of the concatenated average pool and max pool layers) perform better than the corresponding baseline models at epoch 10. In the case of the GC-ViT-T/16 based architecture, the improved model performs better than the baseline model until epoch 9. After that, the baseline model performs better than the improved model. However, in the majority of the cases, the improved model yields better accuracy than the baseline model at the end of the last epoch.  
From Figure 8a, we observe that there is some randomness in the values of accuracy for the train-val-test model, but there is a steep increase in the proposed architecture (ViT-T/16) in the first two epochs, and later it flattens with little variation. It is also seen that there is an improvement in the overall accuracy after 10 epochs for all the techniques. From Figure 8b, we see that after the first three epochs, the accuracy falls in the range of 96 to 98%. Though after seven epochs, there has been a dip observed for the model based on the original ViT architecture (ViT-B/16), but it also stabilizes with more than 98% accuracy later. In Figure 8c, it can be seen that the proposed architecture (ViT-S/16) showed a lot of variations in the accuracy as compared with the other techniques, though the final accuracy after 10 epochs was 98.664. The accuracy does not flatten for different epochs, which can also be observed in that fashion due to the scale on the Y axis. From Figure 8d, we can see that there is a steady increase in the accuracy up to 5 epochs, and then is close to flattening through the next five epochs. For the original ViT architecture (ViT-B/32), though, a large dip can be observed after seven epochs. From Figure 8e, we can observe that there is an  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Experiments on the UBIPr Dataset**} & \multirow{3}{*}{**Models**} & \multicolumn{3}{c}{**CV Score (Accuracy)–ViT Models**} & **Earlier Results** \\ \cline{3-6}  & & **Original** & **Improved** & **Train-Val-Test** & **Train-Val-Test** \\ \cline{3-6}  & & **K-Fold (5)** & **K-Fold (5)** & **6025-1152-3075** & **(6025-1152-3075)** \\ \hline \multirow{3}{*}{**Person Identification (PI)**} & ViT-T/16 & 94.821 & 96.196 & 92.580 & \\ \cline{2-6}  & ViT-B/16 & **98.186** & **98.147** & **96.000** & \\ \cline{1-1} \cline{2-6}  & ViT-S/16 & 94.645 & 96.079 & 92.650 & **93.83**[33] \\ \cline{1-1} \cline{2-6}  & ViT-B/32 & 94.625 & 93.381 & 85.630 & \\ \cline{1-1} \cline{2-6}  & ViT-S/32 & 91.641 & 93.494 & 87.840 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Person identification based on different models.  
increase in the accuracy in a linear fashion for the first three epochs, and later for all the remaining models. However, the values are not varying much, with a steady rise after ten epochs for the ViT-S/32-based models.  
Table 3 depicts the results for gender classification. Compared to person identification, we have obtained a better accuracy in gender classification with the ViT architectures. It can be noted that the proposed model has achieved higher accuracy than the approach presented in [33]. For this comparison, we have performed the train-val-test split, which results in (6025-1152-3075) images.