## 4 Case Study
### Results and Discussion
#### 4.2.2 Cognitive Emotion Recognition
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Number of Initial** & **Number of** & **Number of Test** & \multicolumn{3}{c}{**F1-Score of Each Cognitive Emotion**} & **Average** \\
**Training Samples** & **Selected Samples** & **Samples** & **Frustration** & **Boredom** & **Doubt** & **Pleasure** & **F1-Score** \\ \hline
80 & 100 & 720 & 87.38\% & 84.68\% & 83.09\% & 82.81\% & 84.49\% \\
70 & 110 & 720 & 88.35\% & 84.79\% & 82.02\% & 84.56\% & 84.93\% \\
60 & 120 & 720 & 89.23\% & 84.56\% & 84.57\% & 86.23\% & 86.15\% \\
50 & 130 & 720 & 89.88\% & 86.57\% & 85.32\% & 87.76\% & 87.38\% \\
40 & 140 & 720 & 90.33\% & 87.51\% & 86.83\% & 88.56\% & 88.31\% \\
30 & 150 & 720 & 92.64\% & 88.53\% & 88.25\% & 88.61\% & 89.51\% \\
20 & 160 & 720 & 93.65\% & 89.75\% & 90.54\% & 90.85\% & 91.20\% \\
10 & 170 & 720 & 94.36\% & 90.77\% & 91.19\% & 91.64\% & 91.99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of cognitive emotion recognition based on active learning (proposed method). Figure 9: The confusion matrix with the best average F1-score (91.99%). emphasizing that the average F1-scores for the four cognitive emotions using the proposed method were higher than the best scores from all the competing methods, including another outstanding video-based competing method. This benefit comes from the higher quality training samples obtained by the active learning algorithm. Further, the proposed active learning algorithm not only reduced the cost of labeling (achieving the same accuracy with fewer training samples), but also improved the accuracy when the size of the training set was the same and small. Additionally, the selected higher quality samples can also be used for training and optimizing other models. Since the cost of time, including the time to label the samples, was consider in this study and active learning can obviously reduce the time cost of marking samples, there was no comparison of the time cost between the competing methods and the proposed method. Before the real-time performance test, the recognition model was retrained using the training set to which we added some video data for the expression of cognitive emotions of East Asians. A computer-based cognitive emotion recognition application based on the retrained model was developed for the test. Due to some technical limitations, we had to truncate videos captured in real time to a theoretical minimum duration for cognitive emotions (1 s) and input them into the method framework. Some screenshots of the real-time recognition process are shown in Figure 10. In the test, the tester faced the camera and displayed the four cognitive emotions based on the prompts. The camera collected real-time facial expression videos of the tester, and the application automatically cut the video stream into 1 s video clips or after each click of the \"Recognition\" button. The video clips were subsequently input into the framework of the emotion recognition method, and the retrained model was used for cognitive emotion recognition. Through observation, this method achieved relatively real-time cognitive emotion recognition with a delay time of about 2 s, and the accuracy was still satisfactory. The main reasons for the 2 s delay may be twofold: the large number of feature description points led to a high dimensionality of the feature vector and the RF model had a huge number of parameters, which could be solved by attempting to replace it with SVM or simplifying the RF model into a decision tree model.