Fine-Grained Multi-Attribute Adversarial Learning for Face Generation of Age, Gender and Ethnicity
Lipeng Wan, Jun Wan, Yi Jin, Zichang Tan, Stan Ziqing Li · 2018
Since the Generative Adversarial Network (GAN) was proposed, facial image generation used for face recognition has been studied in recent two years. However, there are few GAN-based methods applied for fine-grained facial attribute analysis, such as face generation with precise age. In this paper, fine-grained multi-attribute GAN (FM-GAN) is presented, which can generate fine-grained face image under specific multiply attributes, such as 30-year-old white man. It shows that the proposed FM-GAN with fine-grained multi-label conditions is better than conditional GAN (cGAN) in terms of image visual fidelity. Besides, synthetic images generated by FM-GAN are used for data augmentation for face attribute analysis. Experiments also demonstrate that synthetic images can assist the CNN training and relieve the problem of insufficient data.