## 1 Introduction
The local pixel structure is learned from the nearest neighbors (KNN) faces. However, there are some uncontrollable problems regarding this method, i.e., the facial images captured by the camera (i.e. LR and facial hallucination)are limited to frontal faces [21]. Therefore, it is practically significant to study how to create HR multi-viewed faces from LR non-frontal images. As a sequence, a method called the multi-viewed facial hallucination method based on the position-patch was developed in [22]. It is a simple face transformation method that converts an LR face image to a global image, predicting the LR multiple views of that given LR image. Based on the synthesized LR faces, facial details are incorporated using the local position-patch. Meanwhile, the traditional locally linear embedding (LLE) [23] technique, when applied in such a hallucination method still faces a problem related to the determination of the optimal weights. Such weights are defined using a fixed number of neighbors for every point [24]. This is not practical for real-world data because the number of neighbors in each point is not equal to the other point. In that study, multi-view face hallucination using an adaptive locally linear embedding technique was proposed for efficiently reconstructing high-resolution face images from a single low-resolution image [25]. The optimal weights determination was applied to manipulate the non-frontal facial details. By feeding a single LR face in one of up, down, left, or right views to the proposed method, the HR images are generated in all views [26]. The critical value of the mapping coefficient in LR to HR is computed in the TRNR [27]. Accuracy is maintained in the training vector using subspace matching, and data with dissimilar scales are computed for representation. The weighted coding methodology is used to evaluate the noisy image as input. Generalized adversarial related networks are used to evaluate the quality of the image using the super-resolution strategy [28]. Dissimilar types of global priority related methodologies prevent the data from segregating into tiny decomposed elements from the input image [29]. The neighbor embedding [30] methodology was used to resolve the natural images, according to the geometry LR patch space construction. The LR training set may be applied to the HR space to demonstrate the image patch. The least-square regression methodology was used to implement location-based patches to regularize coefficient representation to get the most accuracy [31]. The drawbacks of the related methods include not being suitable for global face images while performing super resolving generic image patches. They have minimized accuracy and are time-consuming. While processing the minimized resolution image, the guaranteed output is not achieved. Therefore, the main objectives of that proposed method are:  
1. Using a hybrid methodology, where low-resolution images can be converted into high-resolution based images. 2. Reducing misalignment variations using the local geometric co-occurrence matrix model. 3. Every image with random transformation for each pixel will minimize the computational complexity and minimize the loss to avoid degenerate solutions. 4. Maintaining a high amount of accuracy. Therefore, this paper proposed a new integrated approach based on the iterative super-resolution algorithm and expectation-maximization for face hallucination. Training global face sparse representation was used to reconstruct images with misalignment variations after a local geometric co-occurrence matrix. In the testing phase, we propose a hybrid method, which is a combination of the global sparse representation and local linear regression using the Expectation Maximization (EM) algorithm. Therefore, this work recovers the high-resolution image of a corresponding low-resolution image. Simulation results proved that the proposed work performed well in various performance metrics compared to related works.