## 3 Experimental Results
### MORPH Dataset
#### 3.1.2 Hybrid of the Weighting Fusion Method and UED  
In this subsection, the uniform experimental design (UED) method uses multiple regression analysis to find optimization parameters of two-input CNN based on a weighting fusion method. Table 6 shows the affecting factors and levels of the two-input CNN. The affecting factors include the convolution kernel size, stride size and padding size in the first and fifth convolution layers. Table 7 shows the initial parameters used for the uniform experiment table. Table 8 is the uniform experiment table. This table can be obtained through the calculation steps in Section 2 for subsequent experiments. Finally, the optimization network architecture is obtained. The proposed method combines the weighting fusion method and UED to achieve gender classification in the MORPH dataset. Three cross-validation experiments are performed to obtain a fairer accuracy rate. The accuracy rate of the eight sets in parameter experiments is 99.13%. The optimal network architecture parameters from Table 9 are found and shown in Table 10. Finally, the average accuracy of the gender classification accuracy of this optimized architecture is 99.26%. The average accuracy of the optimized structure for gender classification of the MORPH dataset has indeed improved by 0.13%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Level**} & \multicolumn{3}{c}{**First-Layer Convolution**} & \multicolumn{3}{c}{**Fifth-Layer Convolution**} \\  & **Kernel** & **Stride** & **Padding** & **Kernel** & **Stride** & **Padding** \\ \hline
1 & 9 & 2 & 0 & 3 & 1 & 1 \\
2 & 11 & 4 & 1 & 5 & 2 & 2 \\
3 & 13 & - & 2 & 7 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The affecting factors and levels of the two-input CNN. Figure 4: The average accuracy using various feature fusion methods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Concatenation** & **Product** & **Summation** & **Maximum** & **Weighting** \\ \hline Cross-validation 1 & 0.9766 & 0.9770 & 0.9871 & 0.9837 & 0.9912 \\ Cross-validation 2 & 0.9742 & 0.9782 & 0.9871 & 0.9823 & 0.9914 \\ Cross-validation 3 & 0.9795 & 0.9728 & 0.9867 & 0.9897 & 0.9908 \\ Average accurate & 0.9768 & 0.9757 & 0.9870 & 0.9852 & 0.9911 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The accuracy comparison using various fusion methods in a MORPH dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**UED**} & \multicolumn{3}{c}{**First-Layer Convolution**} & \multicolumn{3}{c}{**Fifth-Layer Convolution**} \\ \multicolumn{1}{c}{**Experimental Parameter Set**} & **Kernel** & **Stride** & **Padding** & **Kernel** & **Stride** & **Padding** \\ \hline
1 & 9 & 2 & 0 & 3 & 1 & 1 \\
2 & 11 & 4 & 1 & 5 & 2 & 2 \\
3 & 13 & 2 & 2 & 7 & 1 & 1 \\
4 & 9 & 2 & 0 & 3 & 2 & 2 \\
5 & 11 & 2 & 1 & 5 & 1 & 1 \\
6 & 13 & 4 & 2 & 7 & 2 & 2 \\
7 & 9 & 4 & 0 & 3 & 1 & 1 \\
8 & 11 & 4 & 1 & 5 & 1 & 1 \\
9 & 13 & 4 & 2 & 7 & 1 & 1 \\
10 & 9 & 4 & 0 & 3 & 2 & 2 \\
11 & 11 & 4 & 0 & 5 & 1 & 1 \\
12 & 13 & 2 & 1 & 7 & 2 & 2 \\
13 & 9 & 4 & 1 & 3 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The initial parameters used of the uniform experiment table. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**UED**} & \multicolumn{3}{c}{**First-Layer Convolution**} & \multicolumn{3}{c}{**Fifth-Layer Convolution**} \\ \multicolumn{1}{c}{**Experimental Parameter Set**} & **Kernel** & **Stride** & **Padding** & **Kernel** & **Stride** & **Padding** \\ \hline
1 & 9 & 4 & 2 & 5 & 1 & 2 \\
2 & 11 & 2 & 1 & 7 & 1 & 1 \\
3 & 13 & 4 & 1 & 5 & 1 & 2 \\
4 & 9 & 4 & 0 & 7 & 2 & 1 \\
5 & 11 & 4 & 0 & 3 & 2 & 1 \\
6 & 13 & 2 & 0 & 7 & 2 & 1 \\
7 & 9 & 2 & 2 & 3 & 1 & 1 \\
8 & 11 & 2 & 2 & 7 & 1 & 2 \\
9 & 13 & 2 & 1 & 3 & 1 & 2 \\
10 & 9 & 4 & 1 & 5 & 2 & 1 \\
11 & 11 & 4 & 0 & 3 & 1 & 2 \\
12 & 13 & 4 & 0 & 5 & 2 & 1 \\
13 & 9 & 4 & 1 & 3 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The uniform experiment table of the two-input CNN.