Facial Attributes Guided Deep Sketch-to-Photo Synthesis

Abstract

Face sketch-photo synthesis is a critical application in law enforcement and digital entertainment industry. Despite the significant improvements in sketch-to-photo synthesis techniques, existing methods have still serious limitations in practice, such as the need for paired data in the training phase or having no control on enforcing facial attributes over the synthesized image. In this work, we present a new framework, which is a conditional version of Cycle-GAN, conditioned on facial attributes. The proposed network forces facial attributes, such as skin and hair color, on the synthesized photo and does not need a set of aligned face-sketch pairs during its training. We evaluate the proposed network by training on two real and synthetic sketch datasets. The hand-sketch images of the FERET dataset and the color face images from the WVU Multi-modal dataset are used as an unpaired input to the proposed conditional CycleGAN with the skin color as the controlled face attribute. For more attribute guided evaluation, a synthetic sketch dataset is created from the CelebA dataset and used to evaluate the performance of the network by forcing several desired facial attributes on the synthesized faces.