Many crimes are committed every day all over the
world, and one of them is a criminal offense that includes a wide
range of illegal acts such as murder, theft, assault, rape,
kidnapping, fraud, and others. Criminals pose a threat to security,
which harms the public interest. In this case, the police question
all eyewitnesses at the crime scene, and sometimes, witnesses who
were present at the crime scene can remember the face of the
criminal. The witness accurately describes the person's facial
features in the report, such as eyes, nose, etc. Law enforcement
authorities use eyewitness information to identify the person.
Criminal investigations can be accelerated by converting sketched
faces into actual images, but this requires eyewitnesses to confirm
the description in the report. Drawings make it very difficult to
identify real human faces because they do not contain the details
that help to catch criminals. In contrast, color photographs
contain many details that help to identify facial features more
clearly. This work proposes to generate color images using the
modified modulation Sketch-to-Face CycleGAN and then pass
them through Generative Facial Prior-GAN. CycleGAN consists
of a generator and discriminator. The generator is used to
generate colored images, and the discriminator is used to identify
whether the images are real or fake. These are then passed to
GFPGAN to improve the quality of the colored images. The
structural similarity index measure of 0.8154 is achieved when
creating photorealistic images from drawings. |
