## 4 Experiments Analysis
### Analysis and Discussion of Results  
Three datasets were used in the proposed facial expression recognition model, and Figure 6 displays the confusion matrix. The JAFFEE model (Figure 6a) had the best overall results in terms of confusion matrix, followed by the KDEF and RaFD datasets (Figure 6c). According to the results, the \"happy\" class had the highest score, regardless of the model. This good prediction was due to two crucial factors: a large dataset and high variance. Training samples are important when highlighting the crucial features of a complex future. This can be seen by the fact that the neutral class overtook the happy class in the training samples. Although it had the largest share of the dataset, the \"neutral\" class failed to generalize as well as others. In the same way, anger, sadness, and surprise classes were compared. The classes shared the same proportion of data from the dataset, but their accuracy changed. In all these observations, emotion variance played a significant role. The confusion matrix also demonstrates the similarity of emotion variation. It is easy to mistake fear for surprise and sad for neutrality. The \"angry\" and \"sad\" classes, for example, had a relatively low variance in mouth and eyebrow shape, which led to a significant number of images being classified as \"neutral\". The shape of the mouth did not change significantly, while the displacement of the eyebrows was hard to distinguish using the CNN model. Interestingly, the same results were not found vice versa, probably because there were so many training samples. Figure 5: A sample facial expression sequence of the KDEF dataset. Three databases were used to run the proposed model. The proposed model produced intermediate feature maps, as shown in Table 2. Based on the confusion matrix given by the JAFFE, KDEF, and RaFD databases, Table 2 shows a classification report. Averaging the classification reports of each database 10-fold produced the classification report. A comparison was also made between the obtained results and those of existing state-of-the-art methods. We compared the proposed model with the other approaches shown in Table 3 and Figure 7. The proposed model showed a 98.18% accuracy score for the JAFFE dataset. The last model is a fusion of several models corresponding to a method's accuracy. Existing models were outperformed by the proposed method. The proposed model combines deep belief networks (DBNs) with facial landmark coordinates to extract hybrid features superior to other deep-learning-based approaches. It is more accurate to classify expressions based on hybrid features. A spatiotemporal CNN with DBN can capture a relative correlation between facial landmarks associated with expressions based on landmark coordinates. Figure 6: Confusion matrix for (**a**) JAFFE; (**b**) KDEF, and (**c**) RaFD datasets. Figure 7: Graphical view of comparative analysis with state-of-the-art methods for the JAFFE dataset [57; 65; 66; 67; 68; 69; 70; 71; 72]. Using the RaFD dataset, the proposed method was compared to the other approaches. The correctness of 93.54% was achieved by combining CNNs, LBPs, and hog descriptors using another author's [74] model, as shown in Table 5 and Figure 9. Meanwhile, [PERSON]. [76] presented an approach that can be used to recognize facial expressions in partial occlusion using Gabor filters and GLCM, which averages an accuracy score of 88.41%. According to ResNet50 [69], a person-independent FER framework was proposed that combines 49 landmarks' shapes and textural features to achieve 84.51% accuracy. The proposed method achieved 98.8% average accuracy compared to the other methods described above. Tables 3-5 show that our proposed model outperformed most previously studied models, although we used the same descriptors. There is a gap in accuracy among the three benchmark facial expression datasets. This improvement has been largely attributed to a multi-class SVM classifier and a CNN with DBN network models.