Enhancing realism in hand-drawn human sketches through conditional generative adversarial network

Abstract

This research focuses on enhancing the realism of hand drawn human sketches through the use of conditional generative adversarial networks (cGAN). Addressing the challenge of translating rudimentary sketches into highfidelity images, by leveraging the capability of deep learning algorithms such as cGANs. This is particularly significant for applications in law enforcement, where accurate facial reconstruction from eyewitness sketches is crucial. Our research utilizes the Chinese University of Hang Kong Face Sketches (CUFS) dataset, a paired dataset of hand drawn human faces sketches and their corresponding realistic images to train the cGAN model. Generator network produces realistic images based on input sketches, where as discriminator network evaluates authenticity of these generated images compared to the real ones. The study involves careful preprocessing of the dataset, including normalization and augmentation, to ensure optimal training conditions. The model performance assessed through both quantitative metrics, such as frechet inception distance (FID), and qualitative evaluations, including visual inspection of generated images. The potential applications of this research extend to various fields, such as agencies of law enforcement for finding suspects and locating missing persons. Future work exploring advanced techniques for further realism, and evaluating the model’s performance across diverse datasets.