# Necessary Morphological Patches Extraction for Automatic Micro-Expression Recognition
## 4 Results and Discussion
### Recognition Performance  
Firstly, we use a LBP-TOP descriptor to extract features in the whole face area, 18 facial active patches, and 10 facial NMP, respectively. Following the original implementations [26; 27], we employ LBP \(-\) TOP\({}_{888331}\) for CASME II and LBP \(-\) TOP\({}_{444113}\) for SMIC. We summarize the results in Table 7. It can be concluded from the experimental result that a higher recognition efficiency can be obtained by using some regions of NMPs, while a lot of redundant features are introduced by using the whole face area. Because of the local characteristic and low intensity, micro expressions involve a few muscle movements when people try to conceal and suppress their true emotions. Therefore, using 18 facial active patches to identify micro-expression can also increase the dimensions and complexities of features, and, meanwhile, reduce the accuracy. Next, we calculate the information entropy to weight the active patches, and then obtain the 10 regions of NMPs with the greatest weight. The weights not only represent the importance of these patches for micro expressions, but also shows the information they could convey. Furthermore, the weighted values of these NMPs generate weighted LBP-TOP features for classifying and recognizing the micro-expressions. The results are shown in Tables 8 and 9. We compare the recognition rates between the dimensionality reduction algorithm and the original method. The weighted LBP-TOP achieves the highest accuracy rate. Compared with other traditional dimensionality reduction algorithms, this proposed algorithm has an obvious advantage that can directly explain the importance of NMPs for micro-expression recognition. The proposed method not only increases the significance of the features, but also improves the robustness of the algorithm. Although the traditional dimensionality reduction algorithm can reduce the high-dimension of the feature in a simple manner, it tends to lose some vital \begin{table}
\begin{tabular}{c c c} \hline \hline
**Facial Region** & **LOSOCV** & **10-Fold** \\ \hline CASME II-whole face & 50.72 & 48.15 \\ CASME II-18 active patches & 56.93 & 52.57 \\ CASME II-10 NMPs & 63.24 & 60.78 \\ SMIC-whole face & 45.04 & 43.98 \\ SMIC-active patches & 53.19 & 50.17 \\ SMIC-10 NMP & 59.80 & 56.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Recognition rate in different regions of micro expression images in the CASME II database (%). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** &
\begin{tabular}{c} **Recognition** \\ **Accuracy (\%)** \\ \end{tabular} & \multicolumn{4}{c}{**Between-Class Accuracy (\%)**} \\ \cline{2-5}  & **Disgust** & **Happiness** & **Representation** & **Surprise** & **Others** \\ \hline LBP-TOP & 63.24 & 56 & 73 & 77 & 38 & 57 \\ LBP-TOP + PCA & 64.67 & 58 & 70 & 79 & 40 & 56 \\ LBP-TOP + LDA & 65.92 & 60 & 70 & 80 & 40 & 57 \\ weighted & 67.95 & 60 & 72 & 83 & 42 & 60 \\ LBP-TOP & \multicolumn{4}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of dimension reduction results (CASME II). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** &
\begin{tabular}{c} **Recognition** \\ **Accuracy (\%)** \\ \end{tabular} & \multicolumn{4}{c}{**Between-Class Accuracy (\%)**} \\ \cline{2-5}  & **Negative** & **Positive** & **Surprise** \\ \hline LBP-TOP & 58.90 & 60 & 64 & 55 \\ LBP-TOP + PCA & 61.36 & 60 & 66 & 58 \\ LBP-TOP + LDA & 62.05 & 62 & 68 & 58 \\ weight LBP-TOP & 64.19 & 64 & 68 & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of dimension reduction results (SMIC). for recognition. Because the micro expression is a subtle facial muscle movement, many useful features would be filtered out as redundant information when applying the traditional algorithm to identify them. Table 10 shows the accuracy rate of different algorithms for micro expression recognition on the CASME II [28; 29; 30; 31]. Our method achieves a higher recognition rate than the others.