Face Attribute Modification Using Fine-Tuned Attribute-Modification Network

Abstract

Multi-domain image-to-image translation with the desired attributes is an important approach for modifying single or multiple attributes of a face image, but is still a challenging task in the computer vision field. Previous methods were based on either attribute-independent or attribute-dependent approaches. The attribute-independent approach, in which the modification is performed in the latent representation, has performance limitations because it requires paired data for changing the desired attributes. In contrast, the attribute-dependent approach is effective because it can modify the required features while maintaining the information in the given image. However, the attribute-dependent approach is sensitive to attribute modifications performed while preserving the face identity, and requires a careful model design for generating high-quality results. To address this problem, we propose a fine-tuned attribute modification network (FTAMN). The FTAMN comprises a single generator and two discriminators. The discriminators use the modified image in two configurations with the binary attributes to fine tune the generator such that the generator can generate high-quality attribute-modification results. Experimental results obtained using the CelebA dataset verify the feasibility and effectiveness of the proposed FTAMN for editing multiple facial attributes while preserving the other details.