# Facial Paralysis Detection on Images Using Key Point Analysis
## 3 Experiments and Results
It was already mentioned that the Toronto NeuroFace (TNF) is a database collected for clinical assessment, and to the best of our understanding and the information provided by the authors, the subset called _Stroke_ can be used to evaluate our methodology. There are 8 subjects with facial asymmetry and 3 participants without it--in total, 817 asymmetrical samples and 219 symmetrical ones. It is important to notice that the TNF images refer to frames extracted from the subjects videos performing oro-facial tasks. Following the proposed methodology, a 10-fold cross-validation technique was employed to train another MLP classifier using samples computed from the TNF database. In Table 4, the performance of the model on the testing part of the data for each fold is presented. Here, the worst performance was 94.56% for the 5 th fold and the best was 98.65% for the 3 rd fold. The average performance of this methodology for the second dataset was 97.22%, as shown in Table 4. A 98.55% correct classification was found after evaluating the MLP classifier with the original 1036 samples. The confusion matrix of the test on the actual TNF database is described in Table 5, which yields a sensitivity of 98.29% and a specificity of 99.54%, proving that our methodology to design a binary classifier to detect facial paralysis can be extended to other databases of face photographs with an outstanding performance. Our methodology detects facial asymmetry levels within an image independently of the gesture performed by the subject, while most of the other methods compute facial asymmetry levels from a set of different facial gestures from the same subject. If we focus on the facial paralysis detection systems (i.e., a binary classification) that compute facial landmarks at some point of their processing, we can put together a summary of three methodologies, as previously introduced in Section 1; such relation is shown in Table 6. We observe that a direct comparison of methods in this table is not feasible because of discrepancies in the goals and metrics used; nonetheless, we include the results of works closer to ours. Those methods compute facial landmarks at some point of their process but train their shape predictor model on their own private dataset (e.g., [9; 10; 11]). Those different performance measures obtained from our tests and shown in Table 6 lead us to suggest that our method exhibits a better performance or, at least, one similar to that of other approaches. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**n-Fold** & **TN** & **TP** & **FN** & **FP** & **Accuracy [\%]** \\ \hline
1 & 66 & 78 & 4 & 0 & 97.30 \\
2 & 65 & 78 & 4 & 1 & 96.62 \\
3 & 66 & 80 & 2 & 0 & 98.65 \\
4 & 65 & 80 & 2 & 1 & 97.97 \\
5 & 64 & 75 & 6 & 2 & 94.56 \\
6 & 65 & 79 & 2 & 1 & 97.96 \\
7 & 66 & 79 & 2 & 0 & 98.64 \\
8 & 65 & 77 & 5 & 0 & 96.60 \\
9 & 65 & 78 & 4 & 0 & 97.28 \\
10 & 64 & 78 & 4 & 1 & 96.60 \\ \hline
**Average** & - & - & - & - & **97.22** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cross-validation results for tests on the TNF database. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Healthy = 0** & **Unhealthy = 1** & **Correct Label** \\ \hline
218 & 1 & healthy \\
14 & 803 & unhealthy \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Resulting confusion matrix of the test on the actual TNF database.