# Human Skeleton Data Augmentation for Person Identification over Deep Neural Network
## 6 Experimental Evaluation and Results
### Results and Comparisons
As shown in the table, the performance of the proposed data augmentation method outperforms the other benchmarked method. Among the benchmarks, \"Benchmark DA #4\" performed the best because the number of augmented training datasets was greater than those of the others. Table 7 shows the average accuracy of the DNN according to the number of dense layers and the number of dense nodes per layer. It was a big issue to determine how many dense layers and dense nodes per layer are needed to guarantee the accuracy of the DNN. For this purpose, we used the grid search method. For explanation, let \(D_{L}\) be the number of dense layers of the DNN and \(D_{N}\) be the number of dense nodes per layer. To find the best parameter configuration, in the experiments, \(D_{L}\) was set to the range of \(\{1,2,3,4,5\}\) and \(D_{N}\) was set to the range of \(\{2^{1},2^{2},\cdots,2^{8},2^{9}\}\). As shown in the table, as \(D_{N}\) increases, the average accuracy of the DNN increases. In other words, if \(D_{N}\) is not sufficiently large, the accuracy of the DNN was not guaranteed. In the experiments, when \(D_{N}=512\) and \(D_{L}\in\{2,3,4,5\}\), the DNN achieved 100% accuracy. Based on the results in the table, we used the DNN consisting of 2 dense layers with 512 dense nodes per layer. To find the best activation function of the DNN, we evaluated the average accuracy of the DNN according to the activation function: the ReLU, sigmoid, softmax, softplus, softsign, tanh, scaled exponential linear unit (SELU) [51], exponential linear unit (ELU) [52], and exponential activation functions. Table 8 shows the evaluation results. In the experiments of the table, the DNN consisting of 2 dense layers with 512 dense nodes per layer was used. As shown in the table, for the ReLU, sigmoid, softplus, softsign, SELU, and ELU activation functions, the DNN achieved 100% accuracy. On the other hand, for the softmax, tanh, and exponential action functions, the accuracy of the DNN was not guaranteed. Therefore, based on the results in the table, we proposed to use one among the six activation functions. In this study, we used the ReLU activation function. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Fold \#1** & **Fold \#2** & **Fold \#3** & **Fold \#4** & **Fold \#5** & **Avg.** \\ \hline Proposed DA & 100.0 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ Benchmark DA \#1 & 52.50 & 59.38 & 62.50 & 52.50 & 68.12 & 59.00 \\ Benchmark DA \#2 & 68.12 & 63.75 & 86.87 & 61.25 & 73.12 & 70.62 \\ Benchmark DA \#3 & 63.13 & 88.75 & 75.63 & 83.75 & 77.50 & 77.75 \\ Benchmark DA \#4 & 64.38 & 87.50 & 90.00 & 84.38 & 76.25 & 80.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison between the proposed and benchmark data augmentation methods. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{5}{c}{**Number of Dense Layers (\(D_{L}\))**} \\ \cline{2-7}  & **1** & **2** & **3** & **4** & **5** \\ \hline \multirow{3}{*}{Number} & 2 & 14.876 & 9.372 & 8.998 & 8.124 & 5.874 \\  & 4 & 44.126 & 28.876 & 27.624 & 21.498 & 16.378 \\  & 8 & 65.502 & 56.626 & 49.998 & 47.248 & 46.750 \\ dense & 16 & 84.674 & 82.548 & 81.500 & 78.752 & 75.626 \\ nodes & 32 & 95.462 & 94.348 & 91.370 & 88.200 & 85.374 \\ per & 64 & 97.498 & 98.700 & 94.670 & 92.266 & 89.736 \\ layer & 128 & 97.983 & 99.120 & 98.316 & 96.189 & 94.586 \\ \((D_{N})\) & 256 & 98.500 & 99.874 & 99.374 & 98.626 & 97.876 \\  & 512 & 99.248 & 100.000 & 100.000 & 100.000 & 100.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison of DNN according to the number of dense layers and the number of dense nodes per layer. To find the best dropout rate of the DNN, we evaluated the average accuracy of the DNN according to the dropout rate. Table 9 shows the evaluation results. In the experiments of the figure, the DNN consisting of 2 dense layers with 512 dense nodes per layer was used. In addition, the ReLU activation function was used in each activation layer of the DNN.