A study [20] built a facial recognition system using feature fusion based on facial features such as eyes, nose, and mouth and extracted global features using principal component analysis and local features using local binary patterns. The features were fused using a Laplacian pyramid and classified using an artificial neural network. The proposed model was tested against challenges such as variations in pose, illumination, expression, image resolution, and occlusion. The same features were further investigated and fused using multi-resolution singular value decomposition [21] and multi-resolution discrete cosine transform [22]. In [23], multi-resolution feature fusion that combined low- and high-resolution features was amplified using Gabor-feature hallucination. This proposed combination increased performance, especially in low-resolution face recognition. A study [24] proposed a hierarchy feature fusion on end-to-end layers in the convolutional neural network to tackle challenges such as illumination and occlusion. In [25], to improve recognition effectiveness, multi-feature fusion using 2D principal component analysis for extracting global features, and local binary pattern for extracting local features, was proposed. Feature fusion on a local binary pattern, elliptical local binary pattern, median binary pattern, and local phase quantization was proposed in [26] as improved local descriptors. In [27], an age-invariant face recognition framework was built using feature fusion and decomposition. The authors presented methods of feature fusion based on recurrent neural network and linear feature fusion. The study [28] assembled a face recognition model based on a multi-scale feature fusion network using a multi-scale bilinear pooled convolutional neural network. In [29], the authors proposed estimated features of frontal face images from fused features based on a generative adversarial network to improve recognition of angled face images. An expression-based convolutional neural network was fused in the layer of multi-level feature fusion and transfer learning convolutional neural network [30] to tackle challenges such as facial expressions in face recognition. In [31], multi-modal biometrics of RGB data and texture descriptors from face and periocular were combined using a fusion strategy in the convolutional neural network. In [16], multi-modal biometrics of ear and face profiles represented by local phase quantization and local directional patterns, reduced by principal component analysis, were fused in a feature-level fusion to improve the overall recognition rate. Looking deeper into image fusion, one strategy to find the best fuse features is, first, to decompose the source images using wavelet decomposition analysis. This process enables us to investigate source images with different scales and frequencies. The study [32] utilized weighted wavelet visual perception fusion. The study aimed to improve underwater images by correcting color and improving global and local contrast of the color-corrected images. The improved-contrast color-corrected images were then decomposed using the wavelet decomposition method. A study [33] investigated three wavelet fusion strategies, max-min, min-max, and mean-mean, to merge charge-coupled device red, green, and blue infrared images and depth-grayscale images. The experiment concluded that the min-max fusion strategy performed best with five levels of decomposition. In [34], feature fusion from the local binary pattern, Gabor, and local directionality pattern of eyebrows, eyes, mouth,and chin features was combined and classified using a support vector machine or artificial neural network. The study [35] proposed employing feature- and pixel-level fusion of wavelet sub-bands for face recognition. The feature-level fusion combined the results of the dimension-reduced approximation sub-band and the results from the pixel-level fusion of detail sub-bands.