Face Image Colorization Based on Generative Adversarial Networks

In this paper, a novel face image colorization method was proposed based on a generative adversarial network. Two groups of generative adversarial sub-networks were involved in the network structure, a generator and a discriminator. One of the sub-networks A (containing generator A and discriminator A) could implement the translation process from gray-scale images to color images, while another sub-network B (containing generator B and discriminator B) could reverse the process. Taking the generated image of sub-networks A as input, the sub-networks B could reconstruct the original gray face image. The whole structure was arranged to ensure the invariance of human face identity, the loop loss in the network was for image reconstruction, the generative loss and adversarial loss were used to make the generated image close to the real image. The experimental results show that this structure can not only achieve a natural and realistic face image, but also ensure that the identity of the face.