Personalized Virtual Makeup Transfer

We propose an example-based personalized virtual makeup transfer approach for digital facial images in order to improve the flexibility of the traditional virtual makeup transfer method.First,given a target image without makeup and several example images with makeup,the algorithm defines different face sub-regions and warps example faces to the target face.Second,the algorithm uses a two-scale decomposition method to obtain the facial structural layer,texture layer,and color layers,and then modifies texture layers of examples to remove blemishes of example faces by using agradient field method.Third,makeup information of each layer of examples is transferred to the corresponding layer of the target image in each sub-region through different ways to obtain a seamless result.Experimental results show that our approach is capable of not only generating high quality personalized makeup results based on multi-examples,but also being applied on harmonization cloning to obtain visually plausible cloning results.