Generate Identity-Preserving Faces by Generative Adversarial Networks
This addresses the problem of identity preservation in face generation for applications like security or entertainment, but it is incremental as it combines existing components.
The paper tackles the challenge of generating high-quality face images that preserve a given identity by proposing a method using generative adversarial networks (GANs) with FaceNet as an identity discriminator, achieving plausible and identity-preserving results.
Generating identity-preserving faces aims to generate various face images keeping the same identity given a target face image. Although considerable generative models have been developed in recent years, it is still challenging to simultaneously acquire high quality of facial images and preserve the identity. Here we propose a compelling method using generative adversarial networks (GAN). Concretely, we leverage the generator of trained GAN to generate plausible faces and FaceNet as an identity-similarity discriminator to ensure the identity. Experimental results show that our method is qualified to generate both plausible and identity-preserving faces with high quality. In addition, our method provides a universal framework which can be realized in various ways by combining different face generators and identity-similarity discriminator.