Generating Photographic Face Images from Sketches: A Study of GAN-based Approaches

Generative Adversarial Networks (GANs) have attracted a lot of attention in recent years due to their potential to advance various fields. The high generative quality of GANs has been harnessed for creating photographic facial portraits from sketches in the field of computer vision. Given the increasing importance of computer vision, the ability to transform hand-drawn sketches into realistic facial images has emerged as a compelling area of research. This practical implication can contribute to diverse fields, including law enforcement, forensics, security, and expedited generation of authentic suspect photos in crime investigations. Despite the inherent lack of specific information in sketch images, the training process necessitates meticulously crafted hand sketches to yield accurate and high-quality results. This paper explores various approaches employed to address the challenges of translating facial sketches into photographic images, with a particular focus on GANs and their applications. The study aims to deliver a comprehensive analysis of state-of-the-art GAN-based methods for generating photographic faces from sketches. By offering a thorough overview of the strengths, methodologies, and advances in this field, this paper aims to pave the way for further advancements in the exciting area of sketch-to-photo face generation. Performance comparisons have been conducted among the different approaches in generating facial images from hand-drawn sketches, showcasing the effectiveness of several GAN architectures, each with a unique set of benefits and drawbacks.