Abstract:
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 handdrawn
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 highquality
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.
