## 3 Results and Discussion
### Comparisons with Other Methods  
This section compares our proposed method results against state-of-the-art image and non-image fusion methods inside face recognition. Table 6 displays the comparisons, and we organized them based on the dataset employed. Our proposed method is comparable to the state-of-the-art methods' results and produces high recognition performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Research** & \
\begin{tabular}{c} **Fusion/Non-** \\ **Fusion** \\ \end{tabular} & **Methods** &
\begin{tabular}{c} **Accuracy/Recognition** \\ **Rate (\%)** \\ \end{tabular} \\ \hline \multirow{6}{*}{AT\&T (ORL)} & \multirow{2}{*}{[60]} & \multirow{2}{*}{Fusion} & Extended local binary patterns and reduction with local non-negative matrix factorization (on unimodal face) & 97.20 \\ \cline{2-4}  & & & unimodal face) & \\ \cline{2-4}  & [22] & Fusion & Multi-resolution discrete cosine transform fusion & 97.70 \\ \cline{2-4}  & [21] & Fusion & Multi-resolution singular value decomposition fusion & 97.78 \\ \cline{2-4}  & [61] & Non-fusion & Convolutional neural network & 98 \\ \cline{2-4}  & [20] & Fusion & Laplacian pyramid fusion & 98.2 \\ \cline{2-4}  & [62] & Non-fusion & Haar-cascade and convolutional neural network & 98.57 \\ \cline{2-4}  & \multirow{2}{*}{Ours} & \multirow{2}{*}{Fusion} & Opposite-frequency features of MRA-DWT/IDWT and image fusion with LP-IF or DWT/IDWT-IF & 99.2 \\ \cline{2-4}  & & & or DWT/IDWT-IF & \\ \hline \multirow{6}{*}{EYB} & [63] & Fusion (graph) & Auto-weighted multi-view semi-supervised learning method & 88.57 \\ \cline{2-4}  & [64] & Non-fusion & Multi-resolution dictionary learning method based on sample expansion & 89.59 \\ \cline{2-4}  & [65] & Non-fusion & Local neighborhood difference pattern & 97.64 \\ \cline{2-4}  & [66] & Fusion & Pre-processing and local phase quantization and multi-scale local binary pattern with score-level fusion and decision-level fusion & 98.30 \\ \cline{2-4}  & \multirow{2}{*}{Ours} & \multirow{2}{*}{Fusion} & Opposite-frequency features of MRA-DWT/IDWT and image fusion with LP-IF or DWT/IDWT-IF & 99.8 \\ \cline{2-4}  & & & or DWT/IDWT-IF & \\ \hline \multirow{6}{*}{EYB-Dark} & [59] & non-fusion & Histogram of oriented gradient and contrast-limited adaptive histogram equalization & 95.4 \\ \cline{2-4}  & \multirow{2}{*}{Ours} & \multirow{2}{*}{Fusion} & Opposite-frequency features of MRA-DWT/IDWT and image fusion with LP-IF or DWT/IDWT-IF & 96.1 \\ \cline{2-4}  & & & or DWT/IDWT-IF & \\ \hline \multirow{6}{*}{FEI} & [67] & Non-fusion & Permutation coding neural classifier based on random local descriptor & 93.57 \\ \cline{2-4}  & [68] & Non-fusion & Integration of the binary-level occurnecematrix and the fuzzy local binary pattern and neural network classifier & 95.27 \\ \cline{2-4}  & [69] & Non-fusion & Principal component analysis & 96 \\ \cline{2-4}  & \multirow{2}{*}{Ours} & \multirow{2}{*}{Fusion} & Opposite-frequency features of MRA-DWT/IDWT and image fusion with LP-IF or DWT/IDWT-IF & 97.5 \\ \cline{2-4}  & & & or DWT/IDWT-IF & \\ \hline \multirow{6}{*}{FEI-FE} & [70] & Non-fusion & Using eye region with Gabor transform and nearest neighbor & 97 \\ \cline{2-4}  & \multirow{2}{*}{Ours} & \multirow{2}{*}{Fusion} & Opposite-frequency features of MRA-DWT/IDWT and image fusion with LP-IF or DWT/IDWT-IF & 99.5 \\ \cline{1-1} \cline{2-4}  & & & or DWT/IDWT-IF & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with other methods (fusion and non-fusion).