  Facial sketches drawn by artists are widely used for visual identification
applications and mostly by law enforcement agencies, but the quality of these
sketches depend on the ability of the artist to clearly replicate all the key
facial features that could aid in capturing the true identity of a subject.
Recent works have attempted to synthesize these sketches into plausible visual
images to improve visual recognition and identification. However, synthesizing
photo-realistic images from sketches proves to be an even more challenging
task, especially for sensitive applications such as suspect identification. In
this work, we propose a novel approach that adopts a generative adversarial
network that synthesizes a single sketch into multiple synthetic images with
unique attributes like hair color, sex, etc. We incorporate a hybrid
discriminator which performs attribute classification of multiple target
attributes, a quality guided encoder that minimizes the perceptual
dissimilarity of the latent space embedding of the synthesized and real image
at different layers in the network and an identity preserving network that
maintains the identity of the synthesised image throughout the training
process. Our approach is aimed at improving the visual appeal of the
synthesised images while incorporating multiple attribute assignment to the
generator without compromising the identity of the synthesised image. We
synthesised sketches using XDOG filter for the CelebA, WVU Multi-modal and
CelebA-HQ datasets and from an auxiliary generator trained on sketches from
CUHK, IIT-D and FERET datasets. Our results are impressive compared to current
state of the art.
