Continuous Emotions: Exploring Label Interpolation in Conditional Generative Adversarial Networks for Face Generation

Abstract

The ongoing rise of Generative Adversarial Networks is opening the possibility to create highly-realistic, natural looking images in various fields of application. One particular example is the generation of emotional human face images that can be applied to diverse use-cases such as automated avatar generation. However, most conditional approaches to create such emotional faces are addressing categorical emotional states, making smooth transitions between emotions difficult. In this work, we explore the possibilities of label interpolation in order to enhance a network that was trained on categorical emotions with the ability to generate face images that show emotions located in a continuous valence-arousal space.