Makeup transfer is a task that can transfer the reference-makeup to the before-makeup face, where makeup style is maintained. It provides a fast and efficient solution for visualizing candidate makeups, receiving extensive academical and industrial attention. However, the difference, such as the human face, eyebrow distance, and lip shape is more or less ignored by recent works, leading to a problem that the facial structures are lost. Moreover, the lack of datasets which are composed of before-makeup and after-makeup images also provides additional difficulties. To this end, this study proposes a regional fast makeup transfer multi-way network. Specifically, the key points of faces are firstly detected and these points are used to align different faces in an end-to-end style. Then, three way-specifical losses are used to optimize the makeup transfer network jointly. These losses are designed with the apparent properties of the eyeshadow, the lipstick, and the foundation, which shows better makeup results. Finally, poisson matting is used to fuse multi-way outputs. Compared to the previous works, the proposed model requires smaller storage space and has faster speed. It can keep the structure of the original face and lead to balancer eyeshadow, more vivid lipstick color, and more detailed foundation make-up. The proposed model is estimated on two universal makeup transfer datasets (i.e., VMU and DLMT). The experimental results show that this proposed method achieves a better visual effect. Besides, it also outperforms the alternatives in terms of makeup speed, the effect of transferring different style to the same person, and transferring the same style to the various people.