The existing makeup transfer algorithms are highly effective with rich features, but they seldom take into account the scenarios of the low-resolution input images. When high-resolution images are difficult to obtain, it will be difficult for the existing makeup transfer algorithms to apply and the makeup cannot be fully transferred. In this study, a makeup transfer algorithm applied to low-resolution images is proposed, which uses the feature matrix containing makeup information as prior information and combines the super-resolution network with the makeup transfer network to produce the synergistic effect. The high-resolution makeup transfer results can be delivered even if the input image is a low-resolution one, and the robustness of postures and expressions is improved while the makeup details are fully retained. Since an end-to-end model is adopted to achieve the makeup transfer and super-resolution, a set of joint loss functions are designed, including generative adversarial loss, perceptual loss, cycle consistency loss, makeup loss, and mean square error loss functions. The proposed model attains an advanced level in both qualitative and quantitative experiments on makeup transfer and super-resolution.