## 6 Discussion
### Comparison with Baseline Models and State-of-the-Art Techniques  
Facial emotion recognition is an important component of human-computer interaction and affective computing. To advance the field, it is necessary to compare new models with traditional baseline methods and state-of-the-art techniques. This paper presents a comparative analysis of various models, including logistic regression, support vector machine (SVM), k-nearest neighbor (k-NN), and CNN, using the FER-2013 dataset.  
To provide a comprehensive and accurate evaluation of facial emotion recognition systems, we compared the performance of the CNN model with a simple baseline model and more complex models from the current literature. The goal of this study is to evaluate the effectiveness of different model architectures and identify potential areas for future improvements.  
Three baseline models were implemented to serve as a reference for performance comparison as follows:  
* Logistic regression is a straightforward linear model often used for classification tasks.
* Support vector machine (SVM) is a powerful non-linear model with a radial basis function (RBF) kernel.
* k-nearest neighbors (k-NNs) is a non-parametric method that classifies data based on proximity in the feature space.  
The effectiveness of these simple models was evaluated on the FER-2013 dataset. The findings detailed in Table 4 acted as a reference point for the assessment of our CNN models.  
We improved our model results, now achieving an accuracy of 64%. The issue lies with the dataset, so we created a methodology and plan to use the AffectNet dataset, which is more efficient when we have additional resources. We experimented with transfer learning techniques such as VGG16, ResNet, and EfficientNet, but the results were around 40%, and the learning time on the local machine was too high.  
To contextualize the performance of our CNN models, we compared them with state-of-the-art models reported in recent studies. EmoNeXt, an adapted ConvNeXt model, and the Residual Masking Network (ResMaskNet) were selected for comparison because of their high accuracy on the FER-2013 dataset. The comparative accuracy is summarized in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Logistic regression & 0.40 & 0.39 & 0.38 & 0.38 \\ SVM & 0.45 & 0.44 & 0.43 & 0.43 \\ k-NN & 0.42 & 0.40 & 0.39 & 0.39 \\ CNN model 1 & 0.62 & 0.60 & 0.59 & 0.59 \\ CNN model 2 & 0.64 & 0.62 & 0.62 & 0.61 \\ \hline \hline \end{tabular}
\end{table}
Table 4: A comparison of the accuracy, precision, recall, and F1-score of the proposed methods.  
The comparative analysis highlights the fact that although our proposed CNN models are better than the simple baseline models for all performance measures, they still fall short of ResMaskNet and EmoNeXt, which are more advanced state-of-the-art models. The two latter models have more complicated architectures and methods; this is why their performance is better than the other models. In particular, ResMaskNet and EmoNeXt employ high-level feature extraction as well as masking techniques, which greatly contribute to increased precision in emotion detection from facial expressions.  
A comparison to the baseline and latest models was performed to establish the relative effectiveness of the proposed model through a comprehensive evaluation. While the proposed CNN models outperform the simpler baseline models, they fall short of state-of-the-art techniques because of a lack of optimization. However, this detailed comparison responds to the reviewer's request and offers insight into potential areas that could be further improved in developing systems for facial emotion recognition.