# Analysis of Facial Cues for Cognitive Decline Detection Using In-the-Wild Data
## 4 Experiments
### Performance Using Eye Blink Features  
The MTs-PD approach was developed to address challenges such as the small range of EAR values and the need for a participant-dependent (PD) threshold calculation. Given the variations in the IVA\({}_{52}\) dataset, including extreme EAR values, we conducted two experiments to evaluate the approach under different conditions. Experiment 1 evaluates the impact of SD and IQR for outlier removal by varying their factors and assessing performance on three-way (ND vs. MCI vs. FMD) and two-way classification tasks. The two classification tasks include (1) memory problems (MPs) versus HCs, where MP consists of ND, MCI, and FMD, and (2) dementia (ND, MCI) versus non-dementia (FMD, HCs), referred to as D vs. Non-D. Experiment 2 applies the best-performing SD and IQR parameters (factors) from Experiment 1 and extends the evaluation to additional classification tasks, including four-way classification. The following subsections provide a detailed comparison of these approaches. 1.1 Experiment 1: Evaluating the Impact of SD and IQR Factor Variations on Classification Performance  
In this experiment, two different threshold-scaling factors are explored for both approaches, SD and IQR. These approaches are used to determine the UB for outlier detection within the MTs-PD approach. The factors range from 1 to 3, increasing by increments of 0.5 (i.e., 1, 1.5, 2, 2.5, 3). First, they are investigated via three classification tasks: a three-way problem (ND vs. MCI vs. FMD) and two two-way problems (HC vs. MP) and (HC/FMD vs. ND/MCI). Four different classifiers were investigated, with the KNN classifier achieving the best performance; hence, it was used in subsequent experiments for consistency. Table 1 presents the results of varying the factor range of the SD and IQR approaches in the three-way classification. The results show that applying IQR to determine the upper boundary improves system performance, unlike SD, which does not improve with factor variation, as illustrated in the confusion matrices in Figure 7. The highest results are obtained with the IQR when factor \(b\) equals 1.5, 2.5, and 3. The difference between the IQR and SD results is statistically significant. Similar trends are shown in the two-way classification task (ND/MCI vs. FMD/HCs), as shown in Table 2 and Figure 8, where the IQR approach outperforms the SD approach, particularly at factor \(b=2\). However, the difference between the SD and IQR results is not statistically significant. In contrast, the classification task HC vs. MP shows that the SD approach gives a better performance than IQR for all the variations. These findings are different to the results in (ND vs. MCI vs. FMD) and (D vs. Non-D) (see Table 3 and Figure 9). From the table and the confusion matrix, it can be seen that the SD gives the highest performance when it equals 1.5. The difference between the results obtained using SD and IQR is considered to be statistically significant. \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) & 44\% & 31\% & 40\% & 32\% \\  & IQR & **49\%** & **51\%** & **51\%** & **51\%** \\ \hline \multirow{2}{*}{1.5} & SD (Min-UB) & 44\% & 31\% & 40\% & 32\% \\  & IQR & **53\%** & **58\%** & **53\%** & **52\%** \\ \hline \multirow{2}{*}{2} & SD (Min-UB) & 44\% & 31\% & 40\% & 32\% \\  & IQR & **50\%** & **51\%** & **48\%** & **45\%** \\ \hline \multirow{2}{*}{2.5} & SD (Min-UB) & 44\% & 31\% & 40\% & 32\% \\  & IQR & **53\%** & **58\%** & **53\%** & **52\%** \\ \hline \multirow{2}{*}{3} & SD (Min-UB) & 44\% & 31\% & 40\% & 32\% \\  & IQR & **53\%** & **58\%** & **53\%** & **52\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification results of the three-way problem—neurodegenerative disorder (ND) vs. mild cognitive impairment (MCI) vs. functional memory disorder(FMD)—using a range of values for SD and IQR to find the factor with the highest performance score.