GAN-Based Face Attribute Editing

Shuang Liu,Tianchi Cao,Junwen Ji,Yingsong Hu,Yuke Sun,Dan Li

doi:10.1109/access.2020.2974043

Abstract

Recently, a variety of methods using the Generative Adversarial Network (GAN) for face editing have been proposed. However, the existing methods cannot control the editing content of the face elements according to the user-specified attributes or need to train a conditional GAN for editing tasks, which means it is difficult to add new attributes in the future. In this paper, a method to edit face attributes by editing the latent variable with the help of a pre-trained unconditional GAN and a linear classification model is proposed. In particular, face attribute editing is divided into two separate stages: Firstly, based on the optimization function, the generative model does a latent variable search to generate a high-quality face image that is similar to the input image. Secondly, by editing the latent variable of the GAN, the attribute of the generated face image can be modified indirectly, so it is almost unaffected by the training process and network structure of GAN, which means it is a flexible method for nearly all GAN network. Images of the FFHQ dataset are edited by attribute labels defined in Celeba dataset for experiments. These experiments prove that our method can edit a variety of face images that vary with race, gender, age, and camera shooting angle. The overall quality of the edited image is not inferior to the other face attribute editing method, and attribute classification for edited image shows a 92.6% attribute editing success rate of the proposed method.

Highlights

Face editing is to edit and modify human face images to make them suitable for personal aesthetics or medical applications
The mask m1 indicates the attribute editing area defined by the user, we find that a larger computing area is beneficial to measure the similarity and contributes to latent variable search
In our method, unlike the traditional Generative Adversarial Network (GAN) trained by minimax loss uses the LG term as part of the optimization function for latent variable search, the GAN trained by Wasserstein distance should use the LWD term to form the optimization function instead of the LWG term

Summary

INTRODUCTION

Face editing is to edit and modify human face images to make them suitable for personal aesthetics or medical applications. Realness of the edited image [5], and Yeh uses the pre-trained unconditional GAN to fill the unknown area of an image by searching for latent variable [6] These methods cannot control the content used to fill the face when editing the face, so they can’t do editing based on user-defined attributes. Some work can edit the face image according to the attributes specified by the user, there are requirements for the structure and training process of the GAN. We show that the method of this paper can be applied to face images of different races, ages, genders, camera shooting angles, and compare our edited image quality with the work of Suzuki et al [8].

RELATED WORK

ATTRIBUTE EDITING

EXPERIMENTS AND ANALYSIS

OPTIMIZATION FUNCTION

Findings

CONCLUSION