# Human Skeleton Data Augmentation for Person Identification over Deep Neural Network
## 6 Experimental Evaluation and Results
### Results and Comparisons
During the training phase, the batch size was set to 1000 and the Adam optimizer was used. As a result, as shown in Figure 15, the proposed DNN achieved 100% person identification accuracy for all cross-validation folds. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Fold \#**} & \multicolumn{3}{c}{**MLP**} \\ \cline{2-4}  & **MLP-10** & **MLP-20** & **MLP-40** \\ \hline
1 & 46.250 & 47.500 & 51.250 \\
2 & 56.880 & 76.250 & 76.880 \\
3 & 72.500 & 80.000 & 81.250 \\
4 & 50.370 & 74.370 & 77.500 \\
5 & 60.620 & 69.380 & 76.880 \\ \hline Avg. & 57.324 & 69.500 & 72.752 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Testing set accuracy (%) of each MLP over 5-fold cross-validation. Figure 14: Training loss and training set accuracy of each MLP over 5-fold cross-validation. The average accuracy of each method is listed in Table 5. Among the benchmark methods, \(k\)-NN achieves the best average accuracy of 86.5%, whereas MLP-10 exhibits the worst with 57.324%. The person identification accuracy of the proposed method (\"Proposed DNN+Proposed DA\") is greater than that of \(k\)-NN by 23.5%. Moreover, in the experiments shown in the table, to validate the effectiveness of the proposed data augmentation method, we trained MLP-10, MLP-20, and MLP-40 using the augmented training dataset and denoted them as \"MLP-10+Proposed DA,\" \"MLP-20+Proposed DA,\" and \"MLP-20+Proposed DA.\" As shown in the table, the proposed data augmentation method improves the person identification accuracy of \"MLP-10+Proposed DA,\" \"MLP-20+Proposed DA,\" and \"MLP-30+Proposed DA\" compared to those of MLP-10, MLP-20, and MLP-40. For MLP-40, the average accuracy is improved by 25.62%. For MLP-20, it is improved by 24.252%. In addition, for MLP-10, it is improved by 18.204%. Therefore, from the results, the proposed data augmentation method is seen to improve the performance of neural network models by preventing overfitting. In addition, the proposed DNN achieves the best performance among all the methods. Table 6 shows the identification accuracy of the proposed and benchmarked data augmentation methods. For the benchmark, the skeleton sequence is divided into several segments with an equal length. In the experiments of the table, we divided the original sequence with five different segment lengths. From the frame division process, 15 segments corresponding to each length are generated as one for \(F\), two for \(F/2\), three for \(F/3\), four for \(F/4\), and five for \(F/5\). Then, the anthropometric feature vectors are calculated for each segment. By doing this, the feature vectors used for training of the DNN can be augmented in the benchmark method. In \"Benchmark DA #1,\" the anthropometric feature vectors were calculated for 3 segments (one for \(F\) and two for \(F/2\)). Therefore, the total number of training datasets becomes 1960 (\(=3\times 640\)). In \"Benchmark DA #2,\" the feature vectors were  
Figure 15: Accuracy boxplots for each method over 5-fold cross-validation. \begin{table}
\begin{tabular}{r c} \hline \hline
**Method** & **Accuracy** \\ \hline C-SVM & 82.625 \\ nu-SVM & 83.000 \\ \(k\)-NN & 86.500 \\ MLP-10 & 57.324 \\ MLP-20 & 69.500 \\ MLP-40 & 72.752 \\ MLP-10 + Proposed DA & 75.528 \\ MLP-20 + Proposed DA & 93.752 \\ MLP-40 + Proposed DA & 98.372 \\ Proposed DNN + Proposed DA & 100.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average accuracy (%) comparison. calculated for 6 segments (one for \(F\), two for \(F/2\), and three for \(F/3\)), and the total number of training datasets becomes 3840 (\(=6\times 640\)). In \"Benchmark DA #3,\" the feature vectors were calculated for 10 segments (one for \(F\), two for \(F/2\), three for \(F/3\), four for \(F/4\)), and the total number of training datasets becomes 6400 \((=10\times 640)\). In \"Benchmark DA #4,\" the feature vectors were calculated for 15 segments (one for \(F\), two for \(F/2\), three for \(F/3\), four for \(F/4\), and five for \(F/5\)), and the total number of training datasets becomes 9600 \((=15\times 640)\).