# VGG16-MLP: Gait Recognition with Fine-Tuned VGG-16 and Multilayer Perceptron
## 4 Experiments and Discussions
### Comparison Results  
In this experiment, the performance of the proposed method is compared with five existing methods, namely GEINet [29], deep CNN [30], CNN with leaky ReLU [31], CNN [34], and deep CNN [37]. All the datasets are partitioned into 80% training, 10% validation, and 10% testing. The input image size of 128 \(\times\) 128 is used for all the existing methods to have an objective comparison. The accuracy of the proposed VGG16-MLP and the existing methods are presented in Table 8.  
As the CASIA-B dataset consists of incomplete silhouettes, the performance of most of the existing methods have marginally decreased, specifically the deep CNN [30] method. Even so, the proposed VGG16-MLP method achieves the highest accuracy as 100%. This is due to the incorporation of the fine-tuned VGG-16 model and multilayer perceptron, which  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Learning Rate** & **Accuracy (\%)** & **Time (s)** \\ \hline
**0.0001** & **100.00** & **695.8941** \\
0.001 & 97.87 & 1134.4350 \\
0.01 & 0.96 & 406.1634 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy at different learning rates \(L\) [\(B=32\), \(P=0.3\), \(\theta=\) Adam].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Optimizer** & **Accuracy (\%)** & **Time (s)** \\ \hline SGD & 99.20 & 2280.8636 \\
**Adam** & **100.00** & **695.8941** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Accuracy at different optimizers \(\theta\) [\(B=32\), \(P=0.3\), \(L=0.0001\)].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & \multicolumn{4}{c}{**Accuracy (\%)**} \\ \cline{2-5}  & **CASIA-B** & **OU-ISIR DB\({}_{high}\)** & **OU-ISIR DB\({}_{low}\)** & **OU-LP** \\ \hline GEINet [29] & 95.66 & 99.86 & 99.72 & 88.34 \\ Deep CNN [30] & 54.82 & 96.73 & 97.15 & 12.02 \\ CNN [31] & 98.75 & 99.86 & 99.65 & 88.88 \\ CNN [34] & 92.94 & 99.10 & 97.85 & 0.0005 \\ Deep CNN [37] & 91.17 & 98.26 & 97.98 & 55.40 \\
**VGG16-MLP** & **100.00** & **100.00** & **100.00** & **99.10** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison results on different datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Learning Rate** & **Accuracy (\%)** & **Time (s)** \\ \hline
**0.0001** & **100.00** & **695.8941** \\
0.001 & 97.87 & 1134.4350 \\
0.01 & 0.96 & 406.1634 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy at different learning rates \(L\) [\(B=32\), \(P=0.3\), \(\theta=\) Adam].  
has the ability to project complex patterns, such as incomplete and noisy silhouettes. Moreover, the effects of the incomplete and noise silhouettes are suppressed, which encourages the performance of the method.  
All the existing methods acquired high accuracy in the OU-ISIR dataset D due to the small number of subjects and clean silhouettes. In both the DB\({}_{high}\) and DB\({}_{low}\) datasets, the proposed VGG16-MLP method obtained 100% accuracy. The experimental results demonstrate that both pre-trained model and multilayer perceptron are able to produce promising performance with both small and large datasets. The pre-trained VGG-16 model represents the features of the GEIs in the feature map, while the multilayer perceptron captures the associations between the feature map and classes. Thus, incorporation of the pre-trained VGG-16 model and the multilayer perceptron contributes to the outstanding performance in the OU-ISIR dataset D.  
Using the OU-LP dataset, the accuracy of the [30; 34; 37] methods are quite low due to the large number of subjects. As the network was developed for a small number of subjects, the performance is quite low. Nonetheless, the proposed method obtained 99.10% accuracy, which shows the scalability and generalization capability of the proposed VGG16-MLP method. The good performance is also attributable to the techniques applied, namely the fine tuning of the pre-trained model, multilayer perceptron, early stopping, the dropout technique, and batch normalization.