# An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification
## 4 Simulation Results
### Case 2: Facial Expression Recognition  
#### 4.2.1 Experiments on the JAFFE and Cohn-Kanade Databases  
Classification is necessary in facial expression recognition; we evaluate the proposed GA-BEL algorithm on two well-known databases, i.e., JAFFE [34] and Cohn-Kanade [35]. The JAFFE database contains 213 grayscale images with 256 \(\times\) 256 pixel resolution of 10 Japanese females. There are seven \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Data Sets** & **Algorithms** & **Precision (\%)** & **Recall (\%)** & **Accuracy (\%)** & **Time(s)** \\ \hline \multirow{4}{*}{Breast Cancer} & SVM & 97.2 & 95.6 & 96.7 & \(5.33\times 10^{-2}\) \\  & LS-SVM & 98.7 & 96.9 & 97.1 & \(2.31\times 10^{-2}\) \\  & BEL & 97.3 & 94.5 & 94.9 & \(8.27\times 10^{-3}\) \\  & **GA-BEL** & 98.5 & 95.6 & **97.5** & \(\textbf{7.63\times 10^{-3}}\) \\ \hline \multirow{4}{*}{Iris} & SVM & 96.3 & 94.4 & 95.1 & \(3.01\times 10^{-2}\) \\  & LS-SVM & 97.9 & 95.3 & 96.7 & \(1.64\times 10^{-2}\) \\  & BEL & 97.2 & 95.8 & 96.6 & \(2.72\times 10^{-3}\) \\  & **GA-BEL** & 99.6 & 97.3 & **97.3** & \(2.93\times 10^{-3}\) \\ \hline \multirow{4}{*}{Banana} & SVM & 89.5 & 87.8 & 88.6 & \(8.37\times 10^{-1}\) \\  & LS-SVM & 88.7 & 86.3 & 87.2 & \(5.79\times 10^{-1}\) \\  & BEL & 91.3 & 88.5 & 88.7 & \(3.31\times 10^{-1}\) \\  & **GA-BEL** & 91.7 & 89.2 & **91.3** & \(\textbf{7.75\times 10^{-2}}\) \\ \hline \multirow{4}{*}{SVMguide1} & SVM & 97.9 & 95.7 & 96.4 & 5.83 \\  & LS-SVM & 96.7 & 94.3 & 95.3 & 4.79 \\  & BEL & 96.2 & 94.5 & 96.5 & 2.43 \\  & **GA-BEL** & 97.3 & 95.6 & **96.9** & **1.81** \\ \hline \multirow{4}{*}{Heart} & SVM & 87.3 & 85.5 & 86.1 & \(3.71\times 10^{-2}\) \\  & LS-SVM & 86.2 & 84.8 & 85.7 & \(1.26\times 10^{-2}\) \\  & BEL & 86.8 & 85.7 & 85.5 & \(5.37\times 10^{-3}\) \\  & **GA-BEL** & 88.7 & 87.3 & **87.8** & \(6.93\times 10^{-3}\) \\ \hline \multirow{4}{*}{Wine} & SVM & 99.3 & 97.1 & 98.2 & \(4.01\times 10^{-2}\) \\  & LS-SVM & 96.5 & 96.4 & 97.9 & \(1.83\times 10^{-2}\) \\  & BEL & 98.5 & 96.3 & 97.4 & \(2.93\times 10^{-3}\) \\  & **GA-BEL** & 98.6 & 96.5 & 97.1 & \(3.51\times 10^{-3}\) \\ \hline \multirow{4}{*}{Satimage} & SVM & 90.7 & 88.5 & 91.1 & 13.7 \\  & LS-SVM & 91.6 & 89.9 & 90.6 & 9.43 \\  & BEL & 92.5 & 90.3 & 91.6 & 3.81 \\  & **GA-BEL** & 94.3 & 92.6 & **93.8** & **2.25** \\ \hline \multirow{4}{*}{Segment} & SVM & 96.2 & 93.7 & 94.5 & 5.78 \\  & LS-SVM & 96.5 & 94.8 & 95.1 & 1.26 \\ \cline{1-1}  & BEL & 96.2 & 94.7 & 94.8 & \(3.81\times 10^{-1}\) \\ \cline{1-1}  & **GA-BEL** & 97.1 & 95.7 & **96.6** & \(\textbf{2.73\times 10^{-1}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparisons of different algorithms. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Sets** & **Study** & **Algorithms** & **Accuracy (\%)** \\ \hline \multirow{2}{*}{Breast Cancer} & [PERSON] [31] & Optimally Pruned Extreme Learning Machine & 95.6 \\  & **This study** & **GA-BEL** & **97.5** \\ \multirow{2}{*}{Banana} & [PERSON] [32] & Extreme Learning Machine & 89.8 \\  & **This study** & **GA-BEL** & **91.3** \\ \multirow{2}{*}{Heart} & [PERSON] [18] & Brain Emotional Learning & 81.3 \\  & **This study** & **GA-BEL** & **87.8** \\ \multirow{2}{*}{Satimage} & [PERSON] [33] & Sparse Extreme Learning Machine & 90.1 \\ \cline{1-1}  & **This study** & **GA-BEL** & **93.8** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification accuracies obtained by the proposed method and the previous methods. facial expressions (angry, surprise, happy, sadness, fear, disgust and neutral), so the facial expression recognition in JAFFE is posed as a seven-class classification problem. The Cohn-Kanade database consists of 2105 digitized image sequences of males and females in the age range of 18-30 years. There are six basic facial expressions (happy, angry, disgust sadness, fear, surprise). Image sequences of each expression are from neutral to peak intensity with the resolution of 640 \(\times\) 480 or 640 \(\times\) 490 pixels.