Generation of Comic Style Chernoff Face with GAN

Abstract

This paper proposes a method for generating comic style Chernoff face with generative adversarial network (GAN) as a first step towards the generation of data comics from multi-dimensional data. The proposed method converts Chernoff face into comic style face images based on the combination of CycleGAN and Pix2Pix. Since both Chernoff face graph and comic images do not have enough information for direct conversion, the Chernoff face graphs are converted into photo style face images and then converted into comic images. A questionnaire asking to rank face images according to the specified impressions is conducted to evaluate the proposed method. The result of the questionnaire shows that the proposed method achieved the same level of consistency among answerers’ judgments as original Chernoff face. It is also confirmed that the proposed method can express the difference in attribute values with mouth parts.