Unsupervised Facial Image Synthesis Using Two Discriminator Adversarial Autoencoder Network
Recent years have witnessed the unprecedented success in single image synthesis by the means of convolutional neural networks (CNNs) High level synthesis of facial image such as expression translation and attribute swap is still a challenging task due to high non linearity. Previous methods suffer from the limitations that being unable to transfer multiple face attributes simultaneously, or incapability of transferring an attribute to another by a continuously changing way. To address this problem, we propose a two discriminator adversarial autoencoder network (TAAN) The latent discriminator is trained to disentangle an input image from its original facial attribute, while the pixel discriminator is trained to make the output image attach to the target facial attribute. By controlling the attribute values, we can choose which and how much a specific attribute can be perceivable in the generated image. Quantitative and qualitative evaluations are conducted on the celebA and KDEF datasets, and the comparison with the state of the art methods shows the competency of our proposed TAAN.
2019 IEEE International Conference on Multimedia and Expo (ICME)