## 3 Results and Discussion
### Results for Other Face Datasets
#### 3.2.2 Results for BeautyREC Face Dataset  
Moving to the next dataset, the BeautyREC Face Dataset was examined to investigate our proposed method against the challenges of face images using makeup. It is highly desirable to consider that the BeautyREC Face Dataset is a result of transferring the makeup style of a reference image to the corresponding components (skin, lips, eyes) of a source image created by [41]. The dataset contains diverse variations in face pose and race. First, we tried our proposed Exp. 5 and 6 method against the BeautyREC face dataset to observe the effect of the total number of images. Figure 16 shows the results of recognition accuracy for our proposed method. We noticed that using all 3000 images created a slight decrease in accuracy. Overall, the method has not yet produced satisfactory results for this dataset. The best result was 46% accuracy using only 1820 images and 44.4% using all images. Next, we observed thirteen variations in our experiments (Figure 17) to find the most suitable setting and parameters to increase recognition performance. We varied the wavelet family for MRA-DWT/IDWT for both fusion with LP-IF (Exp. 5a-Exp. 5e) and DWT/IDWT-IF (Exp. 6a-Exp. 6e). The other wavelet families for MRA-DWT/IDWT (_sym2_, _db2_, _bior2.6_, and _bior 3.3_) did not improve the accuracy. Using _haar_ for MRA-DWT/IDWT and _db2_ for DWT/IDWT-IF, we examined the effect of decomposition levels (Exp. 6f-Exp. 6h). Unfortunately, the variations in the level of decomposition also did not improve the results. We then investigated the wavelet family for DWT/IDWT-IF with _haar_ for MRA  
Figure 15: Accuracy results (%) for the EYB-Dark Face Dataset for Experiment 6 with the mean-mean fusion rule using different wavelet families for MRA-DWT/IDWT. DWT/IDWT and five levels of decomposition (Exp. 6i-Exp. 6k). We found that _db2_ still produced better results. Last, we studied the fusion rules using min-max and max-min (Exp. 6l and Exp. 6m), yet still found that using the mean-mean rule was a better choice. Although the purpose of [41] in creating BeautyREC was to produce a robust makeup transfer dataset and not to test the face recognition system, we still wanted to challenge our proposed method against this case. Regrettably, our proposed method produced only as high as 44.4% accuracy against this dataset. The most probable reasons were high head pose variations and makeup styles (see Figure 18), a non-uniform background, and the BeautyREC Face Dataset being imbalanced, resulting in our proposed method not producing high accuracy results. Figure 16: Accuracy results (%) for the BeautyREC Dataset from Exp. 5 and 6 with variations of employing 1820 images and all (3000) images. Figure 17: Accuracy results (%) for the BeautyREC Dataset: Exp. 5, LP-IF with MRA-DWT/IDWT (a) _haar_, (b) _db2_, (c) _sym2_, (d) _bior2.6_, (e) _bior3.3_; Exp. 6, DWT/IDWT-IF with MRA-DWT/IDWT (a) _haar_, (b) _db2_, (c) _sym2_, (d) _bior2.6_, (e) _bior3.3_; Exp. 6, DWT/IDWT-IF with _haar_ for MRA-DWT/IDWT and _db2_ wavelet with total level of decomposition (f) one, (g) three, (h) seven; Exp. 6, DWT/IDWT-IF with _haar_ for MRA-DWT/IDWT and five levels of decomposition using wavelets (i) _haar_, (j) _sym2_, (k) _bior 2.6_; Exp. 6, DWT/IDWT-IF using fusion rule (l) min-max, (m) max-min. All results came from SVM with the cubic kernel. To examine the effect using an imbalanced and a balanced dataset, we observed the performance of the confusion matrix for the BeautyREC Face Dataset. To create a balanced dataset, we gathered 5 images from 30 people in the dataset, resulting in a total of 150 images to be analyzed. It is also worth noting that we observed how imbalanced the BeautyREC Face Dataset is. There was originally a total of 41 people, and everyone provided a very diverse number of images, ranging from only 1 image to 182 images. The average number of images was 73 images, while the standard deviation was as high as 46.22. These values show that this dataset has a wide range and significant variability.