# Necessary Morphological Patches Extraction for Automatic Micro-Expression Recognition
## 4 Results and Discussion
### NMPs  
The number of facial active patches also affects the performance and recognition rate. Figure 7 shows the relationship between the number of patches and the recognition rate. Even the use of a single crucial patch yields a recognition rate of 31.62%; the use of all 18 active patches produces a recognition rate of 56.93%. We found that the features of some unimportant patches do not play a significant role in identifying micro-expression. Instead of applying all 18 facial active patches, we can extract some crucial patches (NMPs) with discriminant ability for micro-expression recognition. In the figure, N represents the number and location of NMPs. The micro expression only involves a few of facial active patches. The recognition rate reaches to the highest when the number of patches is 10. High values of N contain some extra patches that contribute less to subtle movement and micro expression recognition. Moreover, low values of N lost some important information. The occurrence of micro-expressions is very weak, most of the movements focus on the corners of the eyes, eyebrows, nose, and mouth. The Entropy-Weight method as a feature selection algorithm can evaluate the importance of each feature on the classification problem. This paper compares the contribution of the Entropy-Weight method and other feature selection algorithms in the number of NMPs and recognition accuracy. The experimental results are shown in Table 5. Then, comparing with the data, each algorithm chooses the number of NMPs in the region of the eyes, eyebrows, \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Kernel Function**} & \multicolumn{2}{c}{**CASME II**} & \multicolumn{2}{c}{**SMIC**} \\ \cline{2-5}  & **LOSOCV** & **10-Fold** & **LOSOCV** & **10-Fold** \\ \hline SVM (polynomial) & 64.35 & 61.90 & 60.92 & 59.04 \\ SVM (linear) & 62.89 & 60.02 & 58.57 & 58.39 \\ SVM (RBF) & 67.95 & 65.27 & 64.19 & 62.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Recognition rate of different kernel functions on CASME II and SMIC database (%). \begin{table}
\begin{tabular}{c c|c c|c c} \hline \hline \(R_{X}R_{Y}R_{Y}R_{T}\), **P** & **Accuracy** & \(R_{X}R_{Y}R_{T}\), **P** & **Accuracy** & \(R_{X}R_{Y}R_{T}\), **P** & **Accuracy** \\ \hline
111, 4 & 46.20 & 111, 8 & 47.85 & 111, 16 & 47.02 \\
331, 4 & 49.50 & 331, 8 & 50.33 & 331, 16 & 47.85 \\
333, 4 & 45.37 & 333, 8 & 47.03 & 333, 16 & 47.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Recognition accuracies of different parameters (%). and mouth, which are basically the same while the number for the cheek and nose area is different. This is because the muscle movement of micro-expressions is mainly concentrated in the eye, eyebrow, and mouth regions, and the action units of the cheek and nose regions are very few. Micro-expressions are usually restrained facial movements, which are very subtle and easily overlooked. The Pearson coefficient is insensitive and misleading to these micro-expression areas because of the small correlation between the motions. The lasso model is very unstable, when the data changes slightly, it may lead to great changes in the model. The Entropy-Weight method has the advantages of high robustness and easy use, and the experimental results show that the NMPs selected by this method are basically in line with the most representative facial muscle motion patches proposed by psychologists when micro-expressions occur. In addition, the Entropy-Weight method can give weight to feature vectors, which can better represent the motion characteristics of micro-expressions in the classification process. Each micro expression affects a few specific facial muscles. In other words, only part of the AUs are crucial for micro expression. In this paper, we use Entropy-Weight method to determine the location of NMPs.