## 3 Experimental Results
### Multi-PIE Face Database  
Figure 9 illustrates the different angles and brightness facial expressions of the Multi-PIE dataset. Front facial photos were selected in the experiment and categorized into seven expressions: normal, happy, surprised, squinting, smiling, disgusted, and shouting (Figure 7). Tables 1-3 depict the results of different architectures with the same optimization method and the combination of CNNs using MCNNs-IFI. Tables 4-6 reveal the results of the same architecture with different optimization methods and the combination of CNNs using MCNNs-IFI. The 10-folder CV column in Tables 1-6 indicates the 10 groups of verified data. In those tables, the combination of CNNs using MCNNs-IFI shows higher accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**10-folder CV** & \begin{tabular}{c} **AlexNet** \\ **(Adagrad)** \\ \end{tabular} & \begin{tabular}{c} **GoogleNet** \\ **(Adagrad)** \\ \end{tabular} & \
\begin{tabular}{c} **LeNet** \\ **(Adagrad)** \\ \end{tabular} &
\begin{tabular}{c} **AlexNet(Adagrad) + GoogleNet(Adagrad) +** \\ **LeNet(Adagrad) + IFI** \\ \end{tabular} \\ \hline Round 1 & 92.00\% & 86.00\% & 99.00\% & 100.00\% \\ Round 2 & 92.21\% & 90.85\% & 99.64\% & 99.79\% \\ Round 3 & 89.92\% & 93.25\% & 99.89\% & 99.89\% \\ Round 4 & 92.21\% & 92.25\% & 99.75\% & 99.78\% \\ Round 5 & 92.60\% & 93.60\% & 99.75\% & 99.92\% \\ Round 6 & 92.60\% & 92.32\% & 99.71\% & 99.78\% \\ Round 7 & 91.42\% & 94.00\% & 99.67\% & 99.82\% \\ Round 8 & 92.00\% & 94.53\% & 99.82\% & 99.85\% \\ Round 9 & 91.03\% & 91.42\% & 99.67\% & 99.78\% \\ Round 10 & 91.67\% & 91.67\% & 99.96\% & 99.96\% \\ Average & 91.77\% & 91.99\% & 99.69\% & 99.85\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Recognition results of different architectures with Adam optimization method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**10-folder CV** & \begin{tabular}{c} **AlexNet** \\ **(Adagrad)** \\ \end{tabular} & \begin{tabular}{c} **GoogleNet** \\ **(Adagrad)** \\ \end{tabular} & \
\begin{tabular}{c} **LeNet** \\ **(Adagrad)** \\ \end{tabular} &
\begin{tabular}{c} **AlexNet(Adagrad) + GoogleNet(Adagrad) +** \\ **LeNet(Adagrad) + IFI** \\ \end{tabular} \\ \hline Round 1 & 90.79\% & 93.53\% & 99.96\% & 99.96\% \\ Round 2 & 93.21\% & 93.57\% & 99.86\% & 99.95\% \\ Round 3 & 92.53\% & 93.75\% & 99.89\% & 99.91\% \\ Round 4 & 91.00\% & 91.32\% & 99.79\% & 99.91\% \\ Round 5 & 92.36\% & 85.07\% & 99.71\% & 99.89\% \\ Round 6 & 93.25\% & 94.42\% & 99.64\% & 99.96\% \\ Round 7 & 91.75\% & 92.18\% & 99.82\% & 99.90\% \\ Round 8 & 91.46\% & 91.07\% & 99.75\% & 99.89\% \\ Round 9 & 93.06\% & 95.32\% & 99.64\% & 99.97\% \\ Round 10 & 91.36\% & 90.71\% & 99.82\% & 99.96\% \\ Average & 92.08\% & 92.09\% & 99.79\% & 99.93\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Recognition results of different architectures with stochastic gradient descent (SGD) optimization method.  
Figure 9: Multi-PIE database.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**10-folder CV** & \begin{tabular}{c} **AlexNet** \\ **(Adagrad)** \\ \end{tabular} & \begin{tabular}{c} **GoogleNet** \\ **(SGD)** \\ \end{tabular} & \
\begin{tabular}{c} **LeNet** \\ **(SGD)** \\ \end{tabular} &
\begin{tabular}{c} **AlexNet(SGD) + GoogleNet(SGD) +** \\ **LeNet(SGD) + IFI** \\ \end{tabular} \\ \hline Round 1 & 90.79\% & 93.53\% & 99.96\% & 99.96\% \\ Round 2 & 93.21\% & 93.57\% & 99.86\% & 99.95\% \\ Round 3 & 92.53\% & 93.75\% & 99.89\% & 99.91\% \\ Round 4 & 91.00\% & 91.32\% & 99.79\% & 99.91\% \\ Round 5 & 92.36\% & 85.07\% & 99.71\% & 99.89\% \\ Round 6 & 93.25\% & 94.42\% & 99.64\% & 99.96\% \\ Round 7 & 91.75\% & 92.18\% & 99.82\% & 99.90\% \\ Round 8 & 91.46\% & 91.07\% & 99.75\% & 99.89\% \\ Round 9 & 93.06\% & 95.32\% & 99.64\% & 99.97\% \\ Round 10 & 91.36\% & 90.71\% & 99.82\% & 99.96\% \\ Average & 92.08\% & 92.09\% & 99.79\% & 99.93\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Recognition results of different architectures with stochastic gradient descent (SGD) optimization method.