# Human Skeleton Data Augmentation for Person Identification over Deep Neural Network
## 6 Experimental Evaluation and Results
### Results and Comparisons  
In each cross-validation, the grid search method was used to find the best parameters \(c\) and \(\gamma\) for C-SVM. Figure 12 shows the grid search results for C-SVM over the 5-fold cross-validation. C-SVM achieved an average accuracy of 82.625% over the 5-fold cross-validation. To find the best values of \(g\) and \(\gamma\) parameters for nu-SVM, the grid search method was used in each cross-validation. Figure 13 shows the grid search results for nu-SVM over the 5-fold cross-validation; nu-SVM achieved an average accuracy of 83%. Figure 11: Block diagram of training and testing phases of four benchmark methods. Figure 12: Grid search results (%) for C-SVM over 5-fold cross-validation. Table 3 lists the hyperparameter optimization results for \(k\)-NN over 5-fold cross-validation. As shown in the table, the optimal hyperparameters found at each cross-validation of each dataset were mutually different. In the table, the person identification accuracy of each \(k\)-NN over the 5-fold cross-validation is also listed. The results in the table show that \(k\)-NN achieved an average accuracy of 86.5%. Figure 14 shows the training loss and training set accuracy of each MLP over 5-fold cross-validation. In the experiments, the batch size was set to 64; the Adam optimizer proposed in [50] was used for the training; an ReLU activation function was used; the dropout rate was set to 0.1. As shown in this figure, the convergence speed of the training loss of MLP-40 was the fastest among the MLP-based methods. In addition, the training set accuracy of MLP-40 outperforms that of MLP-10 and MLP-20. As shown in Figure 14, MLP-40 achieves a training set accuracy of approximately 100%, whereas MLP-10 and MLP-20 achieve an accuracy of approximately 60% and 90%, respectively. In particular, in the experiments, it is observed that the training set accuracy of each MLP is lower than 10% when the batch normalization layer is not added. From the results, the batch normalization could be seen to effectively improve the person identification accuracy of MLP-based methods. For this reason, the batch normalization layer was added after the hidden layer of each MLP. \begin{table}
\begin{tabular}{r c c c c c c} \hline \hline \multirow{2}{*}{**Fold \#**} & \multicolumn{5}{c}{**Hyperparameters**} \\ \cline{2-7}  & \(k\) & **Distance Metric** & **Distance Weighting Function** & **Exponent** & **Standardize** & **Accuracy (\%)** \\ \hline
1 & 1 & euclidean & inverse & - & true & 73.125 \\
2 & 3 & cityblock & squaredinverse & - & true & 88.750 \\
3 & 3 & minkowski & squaredinverse & 0.8615 & true & 93.750 \\
4 & 1 & minkowski & equal & 0.8335 & true & 93.750 \\
5 & 2 & minkowski & squaredinverse & 2.1445 & true & 83.125 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyperparameter optimization results for \(k\)-NN over 5-fold cross-validation. Figure 13: Grid search results (%) of nu-SVM over 5-fold cross-validation. Table 4 shows the testing set accuracy of each MLP over the 5-fold cross-validation. As shown in this table, on the testing datasets, MLP-40 achieves the best average accuracy of 72.752%. However, compared with the training set accuracy of MLP-40 in Figure 14, the accuracy of MLP-40 is significantly lower for the testing datasets. The accuracy of MLP-10 and MLP-20 are also lower for the testing datasets, compared with the results in Figure 14. The results show that overfitting occurred in the training phase of each MLP. Figure 15 shows the accuracy of each method over the 5-fold cross-validation. In the figure, the proposed DNN of using the augmented training dataset is denoted \"Proposed.\" In the training phase of each cross-validation, the proposed data augmentation method was applied to 640 anthropometric feature vectors \(\mathbf{V}\). Resulting from the application of the proposed data augmentation method, the total number of \(\mathbf{V}\) became 163,840 \((=640\times 256)\). Using this augmented training dataset, the proposed DNN could be trained without overfitting.