# VGG16-MLP: Gait Recognition with Fine-Tuned VGG-16 and Multilayer Perceptron
## 4 Experiments and Discussions
### Hyperparameter Tuning  
Hyperparameter tuning is essential in order to optimize the performance of the deep learning models. In this work, the hyperparameters were tuned using a grid search technique, which was applied on the CASIA-B dataset. The tuning engages four hyperparameters, i.e., the batch size \(B\), dropout value \(P\), learning rate \(L\), and optimizer \(\theta\). The grid search is performed by altering the value of a particular hyperparameter at a time, while the values of the other three hyperparameters remain constant. The tested and optimal values of hyperparameters for the proposed method are shown in Table 3. Based on the highest accuracy with a low computation time, the optimal value for each hyperparameter is chosen.  
Table 4 displays the accuracy of the proposed VGG16-MLP at different batch sizes \(B\). The batch size at 32 acquired the highest accuracy, even though the time consumed is slightly longer compared to larger batch sizes. The larger batch sizes are computationally expensive and the accuracy is lower than the smaller batch sizes.  
The accuracy of the proposed VGG16-MLP at various dropout rates \(P\) is shown in Table 5. The highest accuracy is obtained at dropout values 0.3 and 0.4. However, the dropout value 0.3 takes less computation time than the dropout value 0.4. The dropout technique is a stochastic regularization that mitigates network overfitting.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Batch Size** & **Accuracy (\%)** & **Execution Time (s)** \\ \hline
**32** & **100.00** & **695.8941** \\
64 & 99.93 & 590.2854 \\
128 & 99.85 & 537.8056 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy at different batch sizes \(B\) [\(P=0.3\), \(L=0.0001\), \(\theta=\) Adam].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Number of Subjects** & **Sequences** & **Angle Views** & **Variations** \\ \hline CASIA-B & 124 & 10 & 11 & Normal walking, carrying condition, clothing \\ OU-ISIR DB\({}_{low}\) & 100 & 370 & 1 & Steady walking \\ OU-ISIR DB\({}_{high}\) & 100 & 370 & 1 & Fluctuated walking \\ OU-ISIR LP (Sequence A) & 3916 & 2 & 4 & 4 viewing angles \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameters** & **Tested Values** & **Optimal Value** \\ \hline Batch Size & 32, 64, 128 & 32 \\ Dropout Value & 0.2, 0.3, 0.4 & 0.3 \\ Learning Rate & 0.0001, 0.001, 0.01 & 0.0001 \\ Optimizer & SGD, Adam & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of hyperparameter tuning and optimal hyperparameter settings for the proposed VGG16-MLP model.  
The accuracy of the proposed method using various learning rates \(L\) is illustrated in Table 6. The highest accuracy is attained when the learning rate is set to 0.0001. A smaller learning rate is more appropriate for a large network than a larger learning rate to reduce overfitting issues.  
Table 7 presents the accuracy of the proposed method at different optimizers \(\theta\). The Adam optimizer is known to be the suitable optimizer when noisy or sparse gradients are involved. Due to this, the Adam optimizer achieved higher accuracy on the CASIA-B dataset than SGD. Furthermore, the Adam optimizer requires less memory and consumes less time to converge, which makes it computationally efficient.