Abstract: In order to further solve the problems such as lack of training data and the wrong makeup area in the existing facial makeup transfer methods, an automatic face makeup migration method based on the generation countermeasure network is proposed. This method constructs the objective function of generative adversarial networks, and achieves the generator by encoder-decoder neural network. Meanwhile, it constructs the discriminator based on multilayer convolutional neural network. The training algorithm adopts alternating optimization. The results of simulation experiment and method comparison show that this method keeps the facial structure, and reflects the reference makeup style as much as possible, achieves a more harmonious makeup effect, has better comparative advantages and visual effects, and provides a new idea for the automatic facial makeup transfer technology.

Key words: facial makeup transfer')">

facial makeup transfer, generate antagonistic network, image style transfer, loss function, generator, discriminator