## 7 Experimental Results
### Performance Analysis with a Variety of Single Classifier Performance  
As mentioned before, the selected features and classifiers are basic. As we aim to prove the stacked based fusion scheme, multiple features and multiple classifiers are beneficial to integrate the advantage of performance and overcome the limitation of a single feature and classifier. Therefore, the overall performance would be improved if each single source, including features and classifiers, is more powerful. To demonstrate this, we discuss the performance when changing the performance of the meta-classifier for facial expression recognition. To do this evaluation, we employ the different types of SVMs, from linear SVM to kernel SVM, with polynomial and radial basis kernel function respectively as the meta-classifier. The parameters are configure as default parameters of Weka's SVM. The first layer is still fusion of three classifiers (IBK/MLP/SMO) and four features (Gab + Geo + LBP + WLD) for AU recognition. The experiment result with the 10-fold approach is shown in Table 9.  
The experiment result demonstrates that overall performance improves when we enhance the single classifier performance. This conclusion could be expended to the first layer doing AU recognition. If processing with a more powerful classifier or feature, the overall performance will increase. However, we prefer to employ the basic classifiers and features to prove our fusing strategy and to prove that even an ensemble with a basic diversity source will, by learning, still achieve the better classification result. In this part, we discuss the classification performance with the confusion matrix to clarify the possible reason causing the false prediction. The confusion matrix of facial expression recognition with the above three SVM as meta-classifiers is shown in Figure 8. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Accuracy (\%)** & **Feature Description** & **MLP** & **SMO** & **IBK** & **NB** \\ \hline \multirow{6}{*}{JAFFE} & Gabor feature & 63.19 & 59.89 & 62.09 & 53.30 \\  & Geometry feature & 83.52 & 79.67 & **84.07** & 78.02 \\  & LBP feature & 46.15 & 45.60 & 47.25 & 47.80 \\  & WLD feature & 78.02 & 71.98 & 75.82 & 70.33 \\  & Gab + Geo + LBP + WLD & **89.07** & **90.16** & 81.42 & **83.61** \\ \hline \multirow{6}{*}{CK} & Gabor feature & 64.97 & 55.65 & 63.84 & 62.99 \\  & Geometry feature & 88.98 & 84.18 & 86.44 & 87.29 \\ \cline{1-1}  & LBP feature & 62.43 & 53.67 & 59.32 & 62.71 \\ \cline{1-1}  & WLD feature & 72.32 & 65.82 & 72.32 & 73.73 \\ \cline{1-1}  & Gab + Geo + LBP + WLD & **94.65** & **91.83** & **85.92** & **93.52** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparing recognition results with simple feature and multi-feature based on AU. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Accuracy (\%)** & **Base Classifiers** & **Gabor Feature** & **Geometry Feature** & **LBP** & **WLD** \\ \hline \multirow{6}{*}{JAFFE} & IBK & 53.30 & 64.29 & 38.87 & 60.85 \\  & MLP & 50.27 & 74.45 & **48.764** & 64.42 \\  & SMO & 50.27 & 60.16 & 42.99 & 60.99 \\  & SMO + IBK + MLP & **59.62** & **81.32** & 46.70 & **74.04** \\ \hline \multirow{6}{*}{CK} & IBK & 53.74 & 74.79 & 50.49 & 58.55 \\  & MLP & 57.98 & 87.64 & 54.94 & 64.55 \\ \cline{1-1}  & SMO & 58.90 & 80.00 & 58.05 & 64.97 \\ \cline{1-1}  & SMO + IBK + MLP & **61.86** & **89.22** & **59.53** & **71.04** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of recognition results with a simple classifier and multi-classifiers based on AU. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**CK** & \begin{tabular}{c} **Linear Support** \\ **Vector Machine** \\ \end{tabular} & \
\begin{tabular}{c} **Polynomial Kernel Support** \\ **Vector Machine** \\ \end{tabular} &
\begin{tabular}{c} **Radial Basis Function Kernel** \\ **Support Vector Machine** \\ \end{tabular} \\ \hline Accuracy (\%) & 91.83\% & 92.39\% & 93.24\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of recognition results with a simple classifier and multi-classifiers based on AU.