# Evaluation of Online Teaching Quality Based on Facial Expression Recognition
## 3 Results  
### Expression Recognition Model Training Results  
When training on the Fer2013 dataset, the network hyperparameters are as follows: The experiment is iterated 300 times. The batch size of the training set is set as 128, the batch size of the verification set is set as 128, and the initial learning rate is set to 0.0001. We optimize the weights of the network through the public validation set, and, finally, the model is tested on the private validation set to evaluate the model performance. The Stochastic Gradient Descent (SGD), shown in Formula (3), is used for optimization, and the categorical crossentropy loss function, shown in Formula (4), is used for model training.  
\[g_{t}=\
abla_{\theta}f(\theta;x_{i},y_{i})+\
abla_{\theta}\phi(\theta) \tag{3}\]  
where \(f(\theta;x_{i},y_{i})\) represents the loss function in each sample, \(g_{t}\) is the gradient, and \(\phi(\theta)\) is the regular term.  
\[Loss=-\sum_{i=1}^{n}y_{i1}\log y_{i1}+y_{i2}\log y_{i2}+\ldots+y_{im}\log y_{im} \tag{4}\]  
Figure 8 shows the accuracy curves of the two methods of VGG16 and VGG16 with ECANet. The accuracy of the two figures will gradually decrease with the increase of epoch times and, finally, become stable. When using VGG16 for model training, the accuracy of the training set will tend to be 0.94, and the accuracy of the validation set tends to be 0.64, while the accuracy of the training set of the VGG16 model with ECANet tends to be 0.95, and the accuracy of the validation set tends to be 0.66.  
The confusion matrix obtained by testing this experimental model is shown in Figure 9. The abscissa is the predicted expression category, and the ordinate is the real expression category. The VGG16 model obtained an accuracy of 64.64% in the FER2013 test set, while the accuracy of the algorithm in this study was 67.40%, a relative improvement of about 2.76%. The accuracy of each category is: anger (58.04%), disgust (63.64%), fear (46.4%), happy (88.40%), sad (48.65%), surprised (79.09%), and neutral (73.32%). Among them, the recognition rate of happy and surprised expressions is relatively high, while the misjudgment rate between fear and sad, two expressions, is relatively high. Since the test set of this dataset has many label errors, the test accuracy is not very high.  
Figure 8: Acc curve. (**a**) VGG16 training acc curve; (**b**) Add ECANet’s VGG16 model training acc curve.  
When using the CK+ data set for model verification, the CK+ data set is divided into a training set, validation set, and test set according to the ratio of 2:1:1. The results obtained by training 150 are shown in Figure 10. The batch size of the training set is 128, the batch size of the verification set is 128, and the initial learning rate is set to 0.0001.  
It can be seen from Figure 10 that the experimental model produced two false positives for the test set made with 245 pictures in the CK+ data set, and the recognition accuracy can reach 99.18%, which can achieve a high recognition effect.  
The classification performance of this our study is measured through multiple measurements on the FER2013 test dataset, and we also compare the classification performance of the model used in this study with other models. For example, the Simple CNN, Simpler CNN, and Tiny XCEPTION models mentioned in [24], and the models used in [25; 26; 27; 28], were compared, and the results are shown in Table 1 below.