## 3 Experimental Results
### CK+ Database  
CK+ database contains 593 sequences across 123 subjects and the images are taken with different brightness levels in Figure 10. All facial photos are categorized into eight expressions: anger, contempt, disgust, fear, happiness, neutral, sadness and surprise and the results are displayed from Tables 7-12. The results of different architectures with the same optimization method and the combination of CNNs using MCNNs-IFI are shown in the first three tables. Tables 10, 11 and 12 reveal the results of the same architecture with different optimization methods and the combination of CNNs using MCNNs-IFI. In those tables, the combination of CNNs using MCNNs-IFI exhibits higher accuracy among the compared methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**10-folder CV** & \begin{tabular}{c} **LeNet** \\ **(Adagrad)** \\ \end{tabular} & \begin{tabular}{c} **LeNet** \\ **(Adam)** \\ \end{tabular} & \
\begin{tabular}{c} **LeNet** \\ **(SGD)** \\ \end{tabular} &
\begin{tabular}{c} **LeNet (Adagrad +** \\ **Adam + SGD) + IFI** \\ \end{tabular} \\ \hline Round 1 & 90.75\% & 99.00\% & 99.96\% & 100.00\% \\ Round 2 & 96.54\% & 99.64\% & 99.86\% & 99.89\% \\ Round 3 & 98.71\% & 99.89\% & 99.89\% & 99.97\% \\ Round 4 & 98.32\% & 99.75\% & 99.79\% & 99.93\% \\ Round 5 & 93.61\% & 99.75\% & 99.71\% & 99.84\% \\ Round 6 & 97.50\% & 99.71\% & 99.64\% & 99.82\% \\ Round 7 & 95.11\% & 99.67\% & 99.82\% & 99.83\% \\ Round 8 & 84.18\% & 99.82\% & 99.75\% & 99.85\% \\ Round 9 & 93.96\% & 99.67\% & 99.64\% & 99.96\% \\ Round 10 & 93.93\% & 99.96\% & 99.82\% & 100.00\% \\ Average & 94.26\% & 99.69\% & 99.79\% & 99.90\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Recognition results of LeNet architectures with different 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} **AlexNet** \\ **(Adam)** \\ \end{tabular} & \
\begin{tabular}{c} **AlexNet** \\ **(SGD)** \\ \end{tabular} &
\begin{tabular}{c} **AlexNet(Adagrad +** \\ **Adam + SGD) + IFI** \\ \end{tabular} \\ \hline Round 1 & 85.82\% & 92.00\% & 90.79\% & 94.21\% \\ Round 2 & 88.14\% & 92.21\% & 93.21\% & 95.89\% \\ Round 3 & 84.71\% & 89.92\% & 92.53\% & 96.79\% \\ Round 4 & 85.54\% & 92.21\% & 91.00\% & 95.89\% \\ Round 5 & 87.25\% & 92.60\% & 92.36\% & 96.43\% \\ Round 6 & 93.57\% & 92.60\% & 93.25\% & 97.21\% \\ Round 7 & 87.18\% & 91.42\% & 91.75\% & 96.07\% \\ Round 8 & 84.18\% & 92.00\% & 91.46\% & 95.93\% \\ Round 9 & 85.36\% & 91.03\% & 93.06\% & 96.14\% \\ Round 10 & 87.50\% & 91.67\% & 91.36\% & 95.96\% \\ Average & 86.93\% & 91.77\% & 92.08\% & 96.05\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recognition results of AlexNet architectures with different optimization method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**10-folder CV** & \begin{tabular}{c} **GoogleNet** \\ **(Adagrad)** \\ \end{tabular} & \begin{tabular}{c} **GoogleNet** \\ **(Adam)** \\ \end{tabular} & \
\begin{tabular}{c} **GoogleNet** \\ **(SGD)** \\ \end{tabular} &
\begin{tabular}{c} **GoogleNet \\ **+ Adam + SGD) + IFI** \\ \end{tabular} \\ \hline Round 1 & 84.25\% & 86.00\% & 93.53\% & 96.96\% \\ Round 2 & 87.11\% & 90.85\% & 93.57\% & 97.21\% \\ Round 3 & 83.89\% & 93.25\% & 93.75\% & 97.07\