Realistic Face Image Generation Based on Generative Adversarial Network

Abstract

Using a computer to generate images with realistic images is a new direction in current computer vision research. This paper designs an image generation model based on the Generative Adversarial Network (GAN). This paper creates a model – a discriminator network and a generator network by eliminating the fully connected layer in the traditional network and applying batch normalization and deconvolution operations. This paper also uses a hyper-parameter to measure the diversity and quality of the generated image. The experimental results of the model on the CelebA dataset show that the model has excellent performance in face image generation.