Abstract: 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.

Doaa M. Mohammed, Mostafa Elgendy and Mohamed Taha, “Forensic Facial Reconstruction from Sketch in Crime Investigation” International Journal of Advanced Computer Science and Applications(IJACSA), 15(9), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150952

@article{Mohammed2024,
title = {Forensic Facial Reconstruction from Sketch in Crime Investigation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150952},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150952},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {9},
author = {Doaa M. Mohammed and Mostafa Elgendy and Mohamed Taha}
}