  The image synthesis technique is relatively well established which can
generate facial images that are indistinguishable even by human beings.
However, all of these approaches uses gradients to condition the output,
resulting in the outputting the same image with the same input. Also, they can
only generate images with basic expression or mimic an expression instead of
generating compound expression. In real life, however, human expressions are of
great diversity and complexity. In this paper, we propose an evolutionary
algorithm (EA) assisted GAN, named EvoGAN, to generate various compound
expressions with any accurate target compound expression. EvoGAN uses an EA to
search target results in the data distribution learned by GAN. Specifically, we
use the Facial Action Coding System (FACS) as the encoding of an EA and use a
pre-trained GAN to generate human facial images, and then use a pre-trained
classifier to recognize the expression composition of the synthesized images as
the fitness function to guide the search of the EA. Combined random searching
algorithm, various images with the target expression can be easily sythesized.
Quantitative and Qualitative results are presented on several compound
expressions, and the experimental results demonstrate the feasibility and the
potential of EvoGAN.
