Kazemi, HadiTaherkhani, FariborzNasrabadi, Nasser M.Brömme, ArslanBusch, ChristophDantcheva, AntitzaRathgeb, ChristianUhl, Andreas2019-06-172019-06-172018978-3-88579-676-4https://dl.gi.de/handle/20.500.12116/23810Face sketch-photo synthesis is a critical application in law enforcement and digital entertainment industry where the goal is to learn the mapping between a face sketch image and its corresponding photo-realistic image. However, the limited number of paired sketch-photo training data usually prevents the current frameworks to learn a robust mapping between the geometry of sketches and their matching photo-realistic images. Consequently, in this work, we present an approach for learning to synthesize a photo-realistic image from a face sketch in an unsupervised fashion. In contrast to current unsupervised image-to-image translation techniques, our framework leverages a novel perceptual discriminator to learn the geometry of human face. Learning facial prior information empowers the network to remove the geometrical artifacts in the face sketch.We demonstrate that a simultaneous optimization of the face photo generator network, employing the proposed perceptual discriminator in combination with a texture-wise discriminator, results in a significant improvement in quality and recognition rate of the synthesized photos. We evaluate the proposed network by conducting extensive experiments on multiple baseline sketch-photo datasets.enSketch-Photo SynthesisGenerative Adversarial Networks (GAN)Unsupervised LearningFacial Geometry Learning.Text/Conference Paper1617-5468