## 3 Experimental Results
### Discussion  
Figure 11 shows that applying the proposed MCNNs-IFI in various CNNs is feasible and effectively provides a high overall recognition rate. Figures 12 and 13 depict the two testing photos, and their recognition results using SGD as the optimal method are presented in Tables 13 and 14. While the complexity is increased by adding more CNNs, the advantages of each CNN can be retained using the proposed method. Besides, with regards to the recognition results, only one CNN can predict correct results, and the other two classifiers provide incorrect results; if a combination of three classifiers is used in the traditional voting concept, the final result will be incorrect.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **AlexNet (SGD)** & **GoogleNet (SGD)** & **LeNet (SGD)** & **AlexNet(SGD) + GoogleNet(SGD) +** \\ \hline Accuracy & 90.89\% & 91.41\% & 97.43\% & 97.94\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Recognition results of AlexNet architectures with different optimization method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **GeogleNet (Adagrad)** & **GoogleNet (Adam)** & **GoogleNet (SGD)** & **GoogleNet (Adagrad + Adam + SGD) +** \\ \hline Accuracy & 88.07\% & 91.92\% & 91.41\% & 96.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: Recognition results of GoogleNet architectures with different optimization method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **AlexNet (Adagrad)** & **GoogleNet (Adam)** & **LeNet (SGD)** & **AlexNet(Adagrad + Adam + SGD) +** \\ \hline Accuracy & 88.07\% & 91.92\% & 91.41\% & 96.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: Recognition results of LeNet architectures with different optimization method.  
Figure 10: CK+ database.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **AlexNet (SGD)** & **GoogleNet (SGD)** & **LeNet (SGD)** & **AlexNet(SGD) +** \\ \hline Accuracy & 90.89\% & 91.41\% & 97.43\% & 97.94\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Recognition results of different architectures with SGD optimization method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **AlexNet (SGD)** & **GoogleNet (SGD)** & **LeNet (SGD)** & **AlexNet(SGD) +** \\ \hline Accuracy & 90.89\% & 91.41\% & 97.43\% & 97.94\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Recognition results of AlexNet architectures with different optimization method.  
Figure 10: CK+ database.  
Figure 11: Recognition accuracy in different CNNs.  
Figure 12: Multi-PIE database example photo (8)-135.  
Figure 13: Multi-PIE database example photo (1)-360.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Classifier** & **Normal** & **Happy** & **Surprised** & **Squinting** & **Smiling** & **Disgusted** & **Shouting** \\ \hline Ground truth & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\ LeNet [1] & 0.99 & 0.00 & 0.01 & 0.01 & 0.00 & 0.00 & 0.00 \\ AlexNet [3] & 0.17 & 0.00 & 0.00 & 0.83 & 0.00 & 0.00 & 0.00 \\ GoogLeNet [4] & 0.01 & 0.00 & 0.00 & 0.99 & 0.00 & 0.00 & 0.00 \\ Proposed method & 0.57 & 0.00 & 0.00 & 0.53 & 0.00 & 0.00 & 0.00 \\ \hline \end{tabular}
\end{table}
Table 13: Example photo (8)-135 recognition results.