Sketch guided face synthesis using conditional variational autoencoder

Forensic sketches, often the only visual leads in criminal cases, typically lack the detail and realism that can help in public identification tasks. This paper presents a Conditional Variational Autoencoder (CVAE) approach for transforming forensic sketches into photorealistic facial images. Using a stochastic edge map extraction of images from a dataset, our model bypasses the need for manually paired sketch-photo databases, enhancing scalability. The model was evaluated on several metrics, demonstrating the capability of working on different image styles.