## 5 Results
### Facial Emotion Recognition Results  
In Table 2, we can see the results for the facial emotion recognizer. The static models (SVC, k-NN, and MLP), trained with the average of the AUs, obtained comparable performances to the sequential model despite the lower computer time consumption and the reduced complexity of these models. Among the static models, the MLP with a single layer of 80 neurons achieved the best accuracy, 58.93%. This metric improved when we normalized the input features between 0 and 1, to 60.22%. These results seems to indicate that the normalization emphasized the differences in the representation of the AUs for each emotion and gave more relevance to AU's binary attributes of presence against the intensity attributes. As an alternative to the static models, we also considered using the AUs extracted from each frame of the videos as individual instances, inheriting the label of their parent video. However, training the models with all the samples and applying a max. voting algorithm to predict the video level class, reported inferior values. For this reason, they are not present in Table 2, but the reader can consult these results in the Appendix B. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Inputs** & **Models** & \begin{tabular}{c} **Hyper-** \\ **Parameters** \\ \end{tabular} & **Norm.** & \begin{tabular}{c} **Accuracy \(\pm\)** \\ **95\% CI** \\ \end{tabular} \\ \hline - & Human perception & - & - & 75.00 \\ \hline - & ZeroR & - & - & \(13.33\pm 1.76\) \\ \hline \multirow{8}{*}{SVC} & \multirow{3}{*}{C = 0.1} & Yes & \(53.25\pm 2.58\) \\ \cline{3-5}  & & & No & \(48.07\pm 2.58\) \\ \cline{3-5}  & & & Yes & \(59.93\pm 2.53\) \\ \cline{3-5}  & & & No & \(54.88\pm 2.57\) \\ \cline{3-5}  & & & Yes & \(55.93\pm 2.56\) \\ \cline{3-5}  & & & No & \(51.65\pm 2.58\) \\ \cline{3-5}  & & & Yes & \(53.10\pm 2.58\) \\ \cline{3-5}  & & & No & \(46.80\pm 2.58\) \\ \cline{3-5}  & & & Yes & \(54.30\pm 2.57\) \\ \cline{3-5}  & & & No & \(49.07\pm 2.58\) \\ \cline{3-5}  & & & Yes & \(55.18\pm 2.57\) \\ \cline{3-5}  & & & No & \(48.82\pm 2.58\) \\ \cline{3-5}  & & & Yes & \(55.40\pm 2.57\) \\ \cline{3-5}  & & & No & \(50.20\pm 2.58\) \\ \hline \multirow{8}{*}{MLP} & \multirow{3}{*}{1 layer (80)} & Yes & \(60.22\pm 2.53\) \\ \cline{3-5}  & & & No & \(58.93\pm 2.54\) \\ \cline{3-5}  & & & Yes & \(57.77\pm 2.55\) \\ \cline{3-5}  & & & No & \(55.82\pm 2.56\) \\ \cline{3-5}  & & & No & \(55.82\pm 2.56\) \\ \cline{3-5}  & & & No & \(62.13\pm 2.51\) \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Sequence \\ of Action Units \\ \end{tabular} } & \multirow{2}{*}{bi-LSTM} & \multirow{2}{*}{
\begin{tabular}{c} 2 bi-LSTM layers (50,50) \\ + 2 attention layers \\ \end{tabular} } & \multirow{2}{*}{No} & \multirow{2}{*}{\(62.13\pm 2.51\)} \\ \cline{3-5}  & & & & \\ \end{tabular}
\end{table} 
Table 2: Quantitative evaluation of the different strategies applied for the facial emotion recognizer. In bold, the best models per input type. Concerning the sequential model, we noticed an increment of 3.2 points regarding the non-normalized top MLP model. This result suggests that there was also relevant information in the temporal structure of the data. Even though this rate was higher than the obtained with the static models, it also revealed that there was still room for improvement on this modality since it still did not surpass human perception accuracy. Moreover, it opened an attractive research line to understand which frames are the most relevant to decide when a video belongs to one or another emotion. This understanding could support generating more precise algorithms for performing emotion recognition on videos. To analyze the causes of the errors, we plotted the confusion matrix of the bi-LSTM experiment that reached an accuracy of 62.13%. We display this matrix in Figure 6.  
The matrix illustrates that most errors happened between 'Sad' and 'Fearful', or between 'Disgusted' and 'Sad'.