The best pixel-level fusion coefficients of detailed sub-bands were found using principal component and linear discriminant analyses. A study [36] used a weighted energy ratio to fuse wavelet packet decomposition images. The fused images were extracted using principal component analysis and the Fisher linear discriminant. A study [37] employed quaternion wavelet transform to decompose images into low-frequency and high-frequency coefficients. The low-frequency coefficients were fused using a weighted-average fusion rule based on phase, magnitude, and spatial variance. Meanwhile, the high-frequency coefficients were fused using the maximum fusion rule based on contrast and energy. The inverse quaternion wavelet transform was performed to obtain the final fused image. Although accuracy and security have significantly increased due to advancements in facial recognition technology, integrating image fusion techniques poses a particular set of challenges. While image fusion results promise better information than the source images, they also introduce the complexity of determining what features or data are being fused. As mentioned, the image fusion approach can be made through a combination of different sensors, multi-modal biometric traits, and dimensional structures. The selected features to be fused need to be as complementary as possible. The overlap of unnecessary information should be minimized [38]. Moreover, it also raises the question of whether the image fusion can retain the visual information of the original image and how to tackle the effect of reduced contrast in the fused image [39]. We proposed using multi-resolution analysis based on discrete wavelet transform (DWT) to decompose images into low-frequency and high-frequency sub-bands. At the same time, the images were filtered using a Gaussian filter to produce blurred images or lower-frequency representation of the images. Then, the Difference of Gaussian was constructed by calculating the differences between the Gaussian blurred images with different sigma. This procedure resulted in high-frequency representation of the images. The low- and high-frequency sub-bands from the decomposition of discrete wavelet transform were then fused with opposite-frequency components from the Gaussian and Difference of Gaussian, i.e., the low-frequency sub-band from DWT was fused with Difference of Gaussian results, and the high-frequency sub-bands from DWT were fused with Gaussian results. These opposite frequencies from the same image reduced the challenges due to the image fusion technique requiring many inputs from several sources and solved the demand that features should be as complementary as possible. The fusion methods employed Laplacian pyramid image fusion (LP-IF) or discrete wavelet transform image fusion (DWT-IF). The fusion rules were as follows: for LP-IF, averaging the base low-pass and highest absolute value for the fused high-pass coefficient; for DWT-IF, we found that the mean-mean rule fusion for both low- and high-frequency coefficients performed the best. To our knowledge, no research has ever proposed the combination of image fusion using discrete wavelet transform decomposition sub-bands with Gaussian filtered images and Difference of Gaussian coefficients. Using the multi-resolution image fusion also helped us to retain and improve the contrast of the original image. The low-frequency components represent the most energy in an image where they contain the slow change's structure and the image's brightness. The fusion result was reconstructed as an enhanced image using inverse discrete wavelet transform. We used this approach to maintain and improve the information from the original image. A histogram of the oriented gradient was employed, and the extracted features were classified using a support vector machine.