Facial makeup transfer with GAN for different aging faces

Abstract

Facial aging is widely used in criminal tracking and the search for lost children. If the aging face is made up, it will greatly affect the discrimination of the tracking system. Therefore, the research on the makeup of different aging faces is extremely important. Existing studies have achieved a good transition from the non-makeup domain to the makeup domain in facial makeup transfer. But few studies involve the transfer of facial makeup at different ages. In addition, existing datasets rarely contain both age and makeup attributes, which make the transfer of facial makeup for different ages full of challenges. To solve the above problems, we propose a learning framework, called AM-Net, which can realize facial makeup transfer for different ages while protecting identity information. AM-Net is composed of two sub-network modules: Aging-Net and Makeup-Net. AM-Net first learns the aging mechanism of faces through Aging-Net, and then, it feeds the learned aging mode to Makeup-Net. After that, AM-Net trains Makeup-Net to realize the mapping relationship between the non-makeup domain to the makeup domain and transfer the makeup style to the face of the non-makeup. Throughout the network, multiple losses are applied to ensure AM-Net preserve information about the identity, background, etc. Extensive experiments are conducted on different datasets with different state-of-the-art methods, which prove the effectiveness of AM-Net.