Facial Expression Transfer using Generative Adversarial Network : A Review

There is high demand of realistic facial expression in current computer graphics and multimedia research. Realistic and accurate facial expression can guarantee the animated character to deliver the expression correctly. However, generating facial expression requires hard work, effort and time since high realism of facial expression need to be in details. There are some available methods in current research area such as face warping to the target, re-use the existing images and also models for generating facial image with certain attribute. Based on literature reviews, current trend for facial expression is using the deep learning method such as generative model like Generative Adversarial Network (GANs). Some of GANs that recently available are Conditional Generative Adversarial Network (cGANs), Double Encoder Conditional GAN (DECGAN), Conditional Difference Adversarial AutoEncoder (CDAAE), Geometry-Guided Generative Adversarial Network (G2GAN), and Geometry-Contrastive Generative Adversarial Network (GC-GAN). These methods actually helped in creating more realistic images, reaching out the realistic facial expression and good identity preservation. This paper aims to review available GANs, find out related features to these methods and also performance of these methods that are useful in facial expression transfer process