## 7 Experimental Results
### Comparison with Several State-Of-The-Art Expression Recognition Methods  
We compared our multi-level fusion based expression recognition method with some work in the current literature using either the CK or JAFFE dataset. Comparisons of recognition accuracies are shown in Tables 11 and 12. The accuracy rate achieved in our proposed approach is highlighted in bold.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Database** & **Mean Accuracy (\%)** & **Angry** & **Disgust** & **Fear** & **Happy** & **Sad** & **Surprise** \\ \hline \multirow{2}{*}{CK} & \multirow{2}{*}{96.62\%} & 95.49\% & 97.18\% & 94.65\% & 97.46\% & 94.93\% & 100.00\% \\  & & (SMO) & (NB) & (SMO) & (NB) & (SMO) & (NB) \\ \hline \multirow{2}{*}{JAFFE} & \multirow{2}{*}{94.63\%} & 89.61\% & 96.72\% & 92.90\% & 96.72\% & 94.54\% & 97.26\% \\  & & (NB) & (SMO) & (NB) & (SMO/NB) & (MLP) & (SMO) \\ \hline \hline \end{tabular}
\end{table}
Table 10: The recognition results of the best performed classifier on CK and JAFFE.  
Figure 11: The results of the multi-layer AU fusion strategy on the JAFFE dataset.  
Figure 10: The results of the multi-layer AU fusion strategy on the CK dataset.  
The results in Tables 11 and 12 show that our proposed fusion method outperforms the others in both CK and JAFFE datasets. Comparing with the deep belief network of [PERSON] et al. [41], our proposed method achieves a comparative recognition rate only 0.1% less than the current prevalent deep learning based method. [PERSON] et al. explored a deep learning method to find the discriminative feature and strong classifiers to recognize expressions. It achieves significantly high recognition results. However, it has a potential risk of over fitting based on the learning scheme. While our proposed method is based on fusion of the various typical features, it overcomes the shortage of dependency on datasets. Actually, the typical features have an advantage in representing the face, as they are derived from the facial analysis problem, compared with the data-driven based deep learning scheme. By making full use of the complementary contribution of different typical features, the generalization ability in representing expression action blocks is improved.  
As listed in Table 11, our proposed approach also has a lower accuracy rate compared with the others, such as that in [PERSON] et al., especially with SVM + boosting feature [23]. This is, to some extent, due to our feature and classifier selection. In this comparison, [PERSON] et al. propose a well-designed dynamic feature, whilst we do not make full use of temporal features of facial expression, which is a significant attribution for expression recognition. In fact, our fusion solution has the great potential to improve the overall performance by reselecting the powerful well-designed features or classifiers. From this viewpoint, what we proposed has the advantage of being a flexible scheme to integrate the different sources, which has no limitation of similar mechanism. In general, our proposed method performs well in different datasets and displays generally good recognition performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline