Towards Criminal Sketching with Generative Adversarial Network

Abstract

Criminal sketching aims to draw an approximation portrait of the criminal suspect by details of the criminal suspect that the observer can remember. However, even for a professional artist, it would need much time to complete sketching and draw a good portrait. It therefore motivates us to study forensic sketching with a generative adversarial network based architecture, which allows us to synthesize a real-like portrait of the criminal suspect described by an eyewitness. The proposed work contains two steps: sketch generation and portrait generation. For the former, a facial outline is sketched based on the descriptive details. For the latter, the facial details are completed to generate a portrait. To make the portrait more realistic, we use a portrait discriminator, which can not only learn the discriminative features between the faces synthesized by the generator and the real faces, but also recognize the face attributes. Experiments have shown that this work achieves promising performance for criminal sketching.