# Facial Paralysis Detection on Images Using Key Point Analysis
## 3 Experiments and Results
It is worth noticing that both databases intend to facilitate information for the development of clinical applications; however, they are not equivalent in terms of image quality, lighting, and pose conditions, and the tasks performed by the participants are not equivalent. In other words, both databases are not directly comparable for our classification problem, but they were helpful in the design process. As stated before, in Section 2.3, the Weka suite was employed at the design stage of our MLP classifier. The suite allowed us to analyze the performance of the MLP classifier parameters--in this case the function named _CVParameterSelection_ was employed [18]. As a result of the analysis, the configuration values were set as learning rate \(L=0.2045\), momentum \(M=0.1909\), training time \(N=500\), seed \(S=0\), and hidden layers \(H=56\). It might be relevant to mention that the 29 features were selected from a set of symmetry measures after evaluating the worth of the features using the strategy implemented by the function named _ClassifierAttributeEval_[18]. Using the 10-fold cross-validation technique, the MLP classifier was trained with 640 samples computed from the MEEI database. Cross-validation is a statistical method used to estimate the performance of machine learning models on new data; it is a widely known technique used to train and evaluate. Later, it was observed that the MLP function did not perform sequential learning, resulting in a negative impact on the classifier performance because the available dataset was unbalanced. That is, there were fewer healthy samples compared to the unhealthy ones (80 vs. 400). To overcome this situation, the healthy set was replicated three times with a sample augmentation process. Similar to the process suggested in [9; 16], the healthy images were rotated in two opposite directions, increasing the amount of available data and also verifying that our algorithm was invariant to rotation. In previous experiments, it was observed that increasing the amount of healthy instances by three times is enough to learn and discriminate this class without over fitting. In the end, the training of the MLP classifier was executed with 640 samples (240 healthy and 400 unhealthy instances). In Table 2, the performance of the model for the testing part of the data for each fold is presented. There, TN stands for true negative (i.e., healthy samples), TP stands for true positive (i.e., unhealthy samples), and false negative (FN) and false positive (FP) are incorrectly classified samples. Here, the worst performance is 84.37% for the 1 st fold and the best is 100% for the 5 th fold. The average performance of this methodology is 94.06%, as shown in Table 2. After evaluating our MLP classifier with the original 480 samples, there is a 99.79% correct classification of the MEEI database, yielding a sensitivity of 99.75% and a specificity of 100%. The confusion matrix of test on the actual MEEI database is given in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**n-Fold** & **TN** & **TP** & **FN** & **FP** & **Accuracy [\%]** \\ \hline
1 & 22 & 32 & 8 & 2 & 84.37 \\
2 & 24 & 37 & 3 & 0 & 95.31 \\
3 & 24 & 33 & 7 & 0 & 89.06 \\
4 & 24 & 38 & 2 & 0 & 96.87 \\
5 & 24 & 40 & 0 & 0 & 100 \\
6 & 23 & 38 & 2 & 1 & 95.31 \\
7 & 24 & 38 & 2 & 0 & 96.87 \\
8 & 22 & 38 & 2 & 2 & 93.75 \\
9 & 24 & 35 & 5 & 0 & 92.19 \\
10 & 24 & 38 & 2 & 0 & 96.87 \\ \hline
**Average** & - & - & - & - & **94.06** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cross-validation results for tests on the MEEI database. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Healthy = 0** & **Unhealthy = 1** & **Correct Label** \\ \hline
80 & 0 & healthy \\
1 & 399 & unhealthy \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Resulting confusion matrix of the test on the actual MEEI database.