# Smart Classroom Monitoring Using Novel Real-Time Facial Expression Recognition System
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dependent Variable**} & \multirow{2}{*}{**Seating Position Last Row**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**Std. Error**} & \multicolumn{3}{c}{**95\% Confidence Interval**} \\ \cline{3-6}  & & & & **Lower Bound** & **Upper Bound** \\ \hline Happy & Yes & 0.121 & 0.036 & 0.050 & 0.191 \\ \hline Sad & Yes & 0.165 & 0.030 & 0.106 & 0.223 \\ \hline Satisfied & Yes & 0.143 & 0.048 & 0.048 & 0.238 \\ \hline Dissatisfied & Yes & 0.396 & 0.039 & 0.318 & 0.473 \\ \hline Concentration & Yes & 0.099 & 0.044 & 0.012 & 0.186 \\ \hline Fear & Yes & 0.099 & 0.020 & 0.060 & 0.137 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical analysis of variable seating position in last rows. in the 30 min's lecture. The feelings of dissatisfaction and sadness were found in the lecture duration of 45 min. Another selected variable \"difficulty of subject\" was analyzed against selected facial expressions. The difficulty level of the subject was analyzed by the students' grades performance the more the failure or the lower grade the students will attain the subject is considered as difficult subject. The theory of automata, multivariable calculus, general science and modern programming languages subjects were considered as the higher difficulty level subjects. The results show that the difficulty level of the subject has a huge impact on the facial expressions of the students. Table 7 below shows that the mean value of the dissatisfied expression (x = 0.78) was higher in the subjects selected with higher difficulty level. Similarly, the mean of another facial expression, sad, is (x = 0.63) while facial expressions such as satisfied is lower, (x = 0.19), indicating that the difficulty level of the subjects had a great impact on the facial expressions of the students. Moreover, the selected subjects with higher difficulty level were analyzed with the time duration of the lecture and the values indicate that the dissatisfied facial expression was found to be at its peak when the lecture duration was 15 min and was lower at 30 min and 45 min, as shown in Table 7.  
The research question \"Does the use of salient facial patches improve the performance the of automatic facial expression recognition system?\" can be answered by stating that adding the salient facial patches increased the accuracy of the automatic facial expression detection system to a greater extent. The statistical measure area under the curve (AUC) and the evaluation metric of accuracy showed improved results while comparing the proposed system with well-known FERs such as Azure, Face ++, and Face Reader. Figures 6 and 7 shows a comparative analysis of the proposed automatic emotion recognition system using facial expression with other well-known FERs on the market. The proposed system was developed to work in a real-time classroom environment but the system is capable of computing the images and videos as well. In order to compare the performance of the proposed system with the existing system the proposed system was provided with images as input because the existing systems can input images only. In Table 8 the commercial FER systems are considered and the F1 score of each emotion is listed using the FER-2013 dataset. In Table 9 the accuracy of the existing FER models is compared with the proposed system using the well-known datasets FER 13, JAFFE, and CK+. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Facial Emotions** & **Emotient** & **Affectiva** & **MorphCast** & **Proposed System** \\ \hline Anger & 0.62 & 0.33 & 0.30 & 0.35 \\ \hline Disgust & 0.65 & 0.40 & 0.49 & 0.76 \\ \hline Fear & 0.40 & 0.24 & 0.36 & 0.44 \\ \hline Happy & 0.75 & 0.75 & 0.76 & 0.90 \\ \hline Sad & 0.70 & 0.38 & 0.54 & 0.89 \\ \hline Surprise & 0.81 & 0.57 & 0.69 & 0.91 \\ \hline Concentration & - & - & - & 0.83 \\ \hline Dissatisfied & - & - & - & 0.57 \\ \hline Satisfied & - & - & - & 0.81 \\ \hline \hline \end{tabular}
\end{table}