Back projection: An effective postprocessing method for GAN-based face sketch synthesis

Abstract

We consider the image transformation problems in this paper, where an input face photo is transformed into a sketch, i.e. face sketch synthesis. It plays important role in video surveillance-based law enforcement. Recent methods for such problems typically train feed-forward convolutional neural networks (CNN) or graphical probabilistic models. In this paper, inspired by the recent success in generating images of generative adversarial networks (GAN), we employ GAN to perform this task. However, accompanying with fine textures generated by GAN model, noise appears among the generated results. We proposed a back projection method to reconstruct the synthesized results. Extensive experiments on public face databases illustrate the effectiveness and superiority of the proposed method compared with state-of-the-art methods. The proposed back projection strategy can be extended to other GAN-based image-to-image translation problems. Data and implementation code in this paper are available online at www.ihitworld.com/WNN/Back_Projection.zip.