A challenging field in image processing and computer graphics is to have higher frequency details by super resolving facial images. Unlike similar papers in this field, this paper introduces a practical face hallucinating approach with higher quality output images. The image reconstruction was based on a set of high and low resolution image pairs. Each image is divided into defined patches with overlapped regions. A patch from a defined location is removed from the low resolution (LR) input image and is compared with the LR patches of the training images with the same location. Each defined LR patch has a defined high resolution (HR) patch. Based on the Euclidean distance comparison, each patch of every single image in the training images database receives a specific weight. This weight is transferred to its relevant HR patch identically. The sum of the gained weights for one specific location of a patch is equal to unity. The HR output image is constructed by integrating the HR hallucinated patches.