Abstract: The traditional Locally Linear Embedding (LLE) technique was applied for face hallucination. This technique determines the optimal weights by the fixed number of neighbors for every point. The previous research, named an Adaptive Locally Linear Embedding (ALLE), referred to a modified version of LLE was proposed to apply with frontal view face hallucination; it uses a threshold of similarity for selecting the neighbors of each point. However, frontal face is barely captured in the real world. Therefore, this study proposes a novel ALLE for multiview face hallucination. The main objective is to generate high quality of frontal and non-frontal face images. The processing steps, according to the proposed method are operated as follows; first, a Low Resolution (LR) face in one of front, up, down, left or right views is fed as an input then the other views of such an LR image are generated by ALLE which applies a threshold of similarity for selecting the neighbors of each point and High Resolution (HR) face images in all views of the same input object are achieved afterwards. The experimental results show that the proposed method yields the better image quality of the reconstructed frontal and non-frontal face images over the baseline methods.