# Analysis of Facial Cues for Cognitive Decline Detection Using In-the-Wild Data
## 4 Experiments
### Performance Using Eye Blink Features
These approaches are tested on 70 thresholds. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factor** & **Approach** & **Accuracy** & **Precision** & **Recall** & **F-Measure** \\ \hline \multirow{2}{*}{1} & SD (Min-UB) & **63\%** & 62\% & 59\% & 58\% \\  & IQR & 62\% & **64\%** & **63\%** & **63\%** \\ \hline \multirow{2}{*}{1.5} & SD (Min-UB) & 67\% & 66\% & 63\% & 63\% \\  & IQR & **71\%** & **72\%** & **71\%** & **71\%** \\ \hline \multirow{2}{*}{2} & SD (Min-UB) & 64\% & 64\% & 62\% & 62\% \\  & IQR & **74\%** & **74\%** & **73\%** & **73\%** \\ \hline \multirow{2}{*}{2.5} & SD (Min-UB) & 67\% & 66\% & 65\% & 65\% \\  & IQR & **71\%** & **72\%** & **71\%** & **71\%** \\ \hline \multirow{2}{*}{3} & SD (Min-UB) & 64\% & 64\% & 61\% & 61\% \\  & IQR & **69\%** & **70\%** & **68\%** & **69\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification results for the two-way problem—dementia (D) vs. non-dementia (Non-D)—using a range of values for standard deviation (SD) and interquartile range (IQR) to identify the factor that yields the highest performance score. These approaches were evaluated across 70 thresholds or features using k-nearest neighbor (KNN) classification with uniform weighting (D includes MCI and ND). Figure 7: The confusion matrices for the three-way classification—neurodegenerative disorder (ND) vs. mild cognitive impairment (MCI) vs. functional memory disorder (FMD)—using two approaches to detect the upper boundary (UB), with a range of factors applied to 70 thresholds. In the confusion matrix, darker colors indicate higher true predicted values, while lighter colors indicate lower predictions (rows: true labels; columns: classified labels). From the results shown above, further analysis is needed by investigating the confusion matrices for a specific classification task. The MP vs. HCs classification task was chosen because it includes all groups, and there is a significant performance difference between the IQR and SD results even when their factors are varied. Figure 9 shows that the common misclassified labels are not from a specific dataset (i.e., \(\text{IVA}_{18}\) or \(\text{IVA}_{34}\)). The incorrect prediction between MP and HC is due to the variation in the recording environments and the devices used. These variations lead to high variations in the range of the EAR calculations that could affect the detection of participants with MP from HC. Interestingly, the HC participants are misclassified when the factor of the SD increases, while the MP participants are correctly classified. For instance, MP participants are classified incorrectly when the factor of the SD is a = 1, although HC participants are classified correctly. Figure 8: The confusion matrices for the two-way classification—dementia(D) vs. non-dementia (Non-D)—using two approaches to detect the upper boundary (UB) with a range of factors applied to 70 features or thresholds. In the confusion matrix, darker colors indicate higher true predicted values, while lighter colors indicate lower predictions. (rows: true labels; columns: classified labels). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factor** & **Approach** & **Accuracy** & **Precision** & **Recall** & **F-Measure** \\ \hline \multirow{2}{*}{1} & SD (Min-UB) & **77\%** & **78\%** & **77\%** & **75\%** \\  & IQR & 63\% & 61\% & 61\% & 61\% \\ \hline \multirow{2}{*}{1.5} & SD (Min-UB) & **81\%** & **80\%** & **80\%** & **79\%** \\  & IQR & 65\% & 63\% & 63\% & 63\% \\ \hline \multirow{2}{*}{2} & SD (Min-UB) & **78\%** & **76\%** & **76\%** & **75\%** \\  & IQR & 69\% & 67\% & 67\% & 67\% \\ \hline \multirow{2}{*}{2.5} & SD (Min-UB) & **78\%** & **76\%** & **76\%** & **75\%** \\  & IQR & 68\% & 67\% & 67\% & 67\% \\ \hline \multirow{2}{*}{3} & SD (Min-UB) & **78\%** & **76\%** & **76\%** & **75\%** \\  & IQR & 68\% & 68\% & 67\% & 67\% \\ \hline \hline \end{tabular}
\end{table}