## 3 Results and Discussion
### Results for AT&T Face Dataset  
#### 3.1.1 Results for All Experiment Designs  
First, we explored the results of the six experiment designs from Table 1 against the AT&T Face Dataset. The AT&T Face Dataset has variations such as lighting conditions, facial expressions, face attributes using glasses and no glasses, and image acquisition with a time gap. Table 3 shows the recognition performance of accuracy results using 1-nearest neighbor (1-NN); support vector machine (SVM) with linear, quadratic, and cubic kernels; and neural network with ReLU (NN-ReLU) classification. We can see from Table 3 that our proposed method (Exp. 5) produced the highest accuracy (99.2%) among the variations. The options of using only _AL_, _HG_, _VG_, and _DG_ fusion features (Exp. 1 and 3) or concatenating the four features (Exp. 2 and 4) did not produce better results compared with the system using the one enhanced reconstructed image (Exp. 5 and 6). Although they did not perform the best, we learned that Exp. 2 and 4 generally produced results as high as the best accuracies from four features from Exp. 1 and 3 (\(AL/HG/VG/DG\)). In terms of image fusion methods, LP-IF (Exp. 1 and 2) and DWT-IF (Exp. 3 and 4), Table 3 shows that both image fusion methods achieved accuracies higher than 97% (SVM) with LP-IF producing the best. In terms of classification method, overall, the SVM yielded better results compared with 1-NN and NN-ReLU. \begin{table}
\begin{tabular}{c c} \hline
**Dataset** & **Challenges** \\ \hline AT\&T & Lighting conditions, facial expressions, glasses and no glasses, \\  & image acquisition with a time gap \\ EYB & Pose, variation in lighting conditions \\ EYB-Dark & Very dark face images (1 normal and 3 dark images) \\ BeautyREC & Face makeup (transfer), imbalanced dataset \\ FEI & 180-degree rotation of the face profile \\ FEI-FE & Neutral and smiling expression \\ \hline \end{tabular}
\end{table} 
Table 2: Face datasets and their challenges. In a more detailed analysis, Table 4 displays the confusion matrix performance for Exp. 5 and Exp. 6, while the confusion matrix chart can be seen in Figures S3 and S4. Table 4 and Figures S3 and S4 show a balanced performance between accuracy, precision, recall, and F1-Score. Our proposed methods (Exp. 5 and 6) were likely reliable and accurate at discriminating across classes. They generalized well throughout the dataset and did not overfit any one class or subset of the data. Table 4 also shows the results if there were only five images per individual/class, and the results were compared with using full (10) images per individual/class. There was a decrease in accuracy of approximately 3.75% to 5.5% if our proposed method employed only five images per class. We calculated the processing time for our proposed methods, Exp. 5 and 6, against the AT&T Face Dataset. These calculations were applied to Exp. 5 and Exp. 6 with variations in level of decomposition in the DWT/IDWT-IF. Figure 7 displays the results of the processing time in seconds. T1 shows the time taken from the input to the creation of the one big matrix (before classification), T2 shows the time taken from the input to the output of accuracy using SVM quadratic, and T3 shows the time taken from the input to the output of accuracy using SVM cubic. We also observed a significant time difference, approximately 87 s, between T1 for Exp. 5 and Exp. 6. DWT/IDWT-IF required much more time than LP-IF. We were not surprised to find an increase in the inference time from Exp. 6a to Exp. 6d. This increase happened due to the rise of the total decomposition levels; the higher the decomposition level in DWT/IDWT-IF, the more time it took. We also calculated the inference time, the time required for one input image processed by our simulation until the system produced the output (prediction). The average inference time was 0.0093 s.