## 4 Conclusions  
In this research, we proposed a pixel-level image fusion of opposite frequencies from MRA-DWT/IDWT sub-bands with Gaussian-filtered results (high-frequency from the wavelet sub-bands and low-frequency from the Gaussian filtered) and MRA-DWT/IDWT sub-bands with Difference of Gaussian (low-frequency from the wavelet sub-bands and high-frequency from the Difference of Gaussian). The pixel-level fusion of creating one enhanced reconstructed image (Exp. 5 and 6) outperformed other possibilities inside the proposed method (Exp. 1-Exp. 4). The proposed method was challenged against AT&T, EYB, EYB-Dark, BeautyREC, FEL, and FEI-FE Face Datasets. The results showed that our proposed method was robust and accurate against challenges such as lighting conditions, facial expressions, head pose, 180-degree rotation of the face profile, dark images, acquisition with a time gap, and conditions where the person uses attributes such as glasses. The proposed method is comparable to state-of-the-art methods and generates high recognition performance. The proposed method successfully achieved an accuracy of 99.2% for AT&T, 99.8% for EYB, 96.1% for EYB-Dark, 97.5% for FEI, and 99.5% for FEI-FE Face Dataset. The detailed results of the best parameters were as follows: overall, LP-IF and DWT/IDWT-IF both produced the best performance, but notably, DWT/IDWT-IF with _sym2_ performed better when tested against challenges such as dark images; the mean-mean fusion rule for five levels of decomposition DWT/IDWT-IF always yielded the best accuracy, while the support vector machine was undeniably the best classifier. Our proposed method did not produce satisfactory results against the makeup face challenge (BeautyREC) due to the high variations in head pose, makeup style, and non-uniform background, and due to this dataset being an imbalanced dataset with a vast range in the number of images. For future development, we suggest the research use pre-processing methods, such as background removal and facial landmark detection. We also know that our proposed algorithm has a relatively long process and sequence of steps, so we aim to simplify the algorithm in future research while maintaining the recognition performance. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/a17110529/s1](https://www.mdpi.com/article/10.3390/a17110529/s1)]([https://www.mdpi.com/article/10.3390/a17110529/s1](https://www.mdpi.com/article/10.3390/a17110529/s1)), Figure S1: The first possibility of variations inside our proposed method: choose AL or HG or VG or DG; Figure S2: The second possibility of variations inside our proposed method: a concatenated vector of extracted HoG from AL, HG, VG, and DG; Figure S3: The row-normalized confusion matrix from Exp. 5 using SVM with (a) quadratic kernel and (b) cubic kernel; Figure S4: The row-normalized confusion matrix from Exp. 6 using SVM with (a) quadratic kernel and (b) cubic kernel; Figure S5: Accuracy results (%) for the EYB-Dark Face Dataset from Experiment 6 using the db2 wavelet in DWT/IDWT-IF and sym2 in MRA=DWT/IDWT with variations in level of decomposition; Figure S6: Accuracy results (%) for the EYB-Dark Face Dataset from Experiment 6 using various wavelet families in DWT/IDWT-IF with five levels of decomposition and sym2 in MRA-DWT/IDWT; Figure S7: The row-normalized confusion matrix from Exp. 5 using SVM with (a) quadratic kernel and (b) cubic kernel for the imbalanced BeautyREC Face Dataset; Figure S8: The row-normalized confusion matrix from Exp. 6 using SVM with (a) quadratic kernel and (b) cubic kernel for the balanced BeautyREC Face Dataset. Conceptualization, R.L. ; methodology, R.L. and M.A. ; software, R.L. and J.A. ; validation, J.A. and M.A. ; investigation, R.L. ; writing--original draft preparation, R.L. ; writing--review and editing, J.A. ; supervision, J.A. and M.A. ; project administration, J.A. All authors have read and agreed to the published version of the manuscript.