Face Makeup Transfer Algorithm Based on CycleGAN

Face makeup transfer is a popular research direction in the domain of computer vision, where makeup style is considered a special style of facial images, and makeup transfer is a part of style transfer. The process of human face makeup transfer is to transfer the makeup of the reference makeup image to the original makeup image while keeping the facial characteristics of the original makeup image. Currently, Generative Adversarial Networks (GANs) have become the mainstream technology for solving the problem of face makeup transfer. Face makeup transfer will be widely used in the future, such as in beauty cameras, makeup software for one-click makeup, virtual makeup trials for cosmetics, and video makeup transfer (such as live streaming software makeup and TV drama character makeup), and has received widespread attention in recent years. It has significant research value both in academia and industry. In this study, the paper adopted a series of innovative strategies to optimize model performance, including the introduction of non-local attention mechanisms to enhance feature extraction capabilities, improved residual blocks to increase network depth and efficiency, and attention modules in discriminators to improve focus on key features. Additionally, the paper used multi-scale discriminators to capture feature information at different levels and added skip connections to promote gradient flow and information integration, thereby achieving more efficient network training and better performance.