## 3 Results and Discussion
### Results for Other Face Datasets  
#### 3.2.1 Results for EYB and EYB-Dark Face Datasets  
The following investigation evaluated the EYB Face Dataset using our proposed method. Having variations in pose and lighting conditions, the EYB Face Dataset was examined. From a total of six experiments, we investigated only Exp. 2, 4, 5, and 6 since the last experiment showed that using concatenated vectors (Exp. 2 and 4) generally performed as well as the highest accuracy from each fused coefficient (Exp. 1 and 3). We examined Exp. 2, 4, 5, and 6 using only SVM for classification due to the best results produced by the SVM in the last experiment. Figure 11 shows the accuracy results of these settings. We saw that the results from Exp. 2, 4, and 5 produced almost the same highest value. The best recognition performance of accuracy was 99.8%. Figure 10: Accuracy results (%) for AT&T Face Dataset from Experiment 6 (proposed method) using various wavelet families in DWT/IDWT-IF with five levels of decomposition and _bior3.3_ in MRA-DWT/IDWT. Figure 9: Accuracy results (%) for AT&T Face Dataset from Experiment 6 (proposed method) using _db2_ wavelet in DWT/IDWT-IF and _bior3.3_ in MRA-DWT/IDWT with variations in the level of decomposition. To challenge our proposed method further against lighting conditions, we evaluated it against the EYB-Dark Face Dataset. Figure 1d shows that this subset of the EYB Face Dataset has only four images for each individual, where one image is considered to use better lighting, and three other images are dark. The same experiment was operated for the EYB-Dark Face Dataset, i.e., Exp. 2, 4, 5, and 6. Figure 12 shows the results of recognition performance. Overall, the accuracy was not as high as the EYB Face Dataset and was reduced by approximately 3.7-43.9%. This decrement is understandable, since the EYB-Dark has only four images per person, three of which are dark (Figure 1d). While Exp. 2 and 4 failed to produce high accuracy, our proposed method, Exp. 5 and 6, generated better results with the highest performance, achieving 93.4%. To further investigate and generate better results, we examined the fusion rule of mean-mean, min-max, and max-min on the EYB-Dark Face Dataset for Experiment 6. Figure 13 displays the results and shows that the mean-mean rule produced the best result between the three different fusion rules. Our results showed different outcomes than those of the study in [33], which found that using the min-max fusion rules provided the best recognition results for hand gesture intention. Figure 11: Accuracy results (%) for the EYB Face Dataset for Experiments 2, 4, 5, and 6. Figure 12: Accuracy results (%) for the EYB-Dark Face Dataset for Experiments 2, 4, 5, and 6. Figure 14 shows the results of DMW/IDWT-IF using each fusion rule. Figure 14 shows why the mean-mean rule performed best, followed by the min-max rule. The mean-mean rule produced results that made details previously obscured by the darkness became visible. While the min-max rule also brought up obscured details, it was not as informative as the mean-mean rule. Moreover, we varied the wavelet family for MRA-DWT/IDWT. The results are shown in Figure 15. This experiment found a better wavelet filter for this challenge inside the EYB-Dark Face Dataset. Using the _sym2_ wavelet improved the recognition by 2.7%, achieving an accuracy of 96.1%. Figure 14: Fusion results of DWT/IDWT-IF with d2 and five levels of decomposition (from left to right) top: original image, using min-max rule, max-min rule, and mean-mean rule; bottom: fusion results but scaled based on the pixel value range. Figure 13: Accuracy results (%) for the EYB-Dark Face Dataset for Experiment 6 using fusion rules: mean-mean, min-max, and max-min. Correspondingly, we investigated the effect of the level of decomposition and various wavelet families in DWT/IDWT-IF for Experiment 6 with the same settings as in Figures 9 and 10 for the AT&T Face Dataset. The same conclusion was achieved, which was that using five levels of decomposition produced the best results.