  In biomedical research and artificial intelligence, access to large,
well-balanced, and representative datasets is crucial for developing
trustworthy applications that can be used in real-world scenarios. However,
obtaining such datasets can be challenging, as they are often restricted to
hospitals and specialized facilities. To address this issue, the study proposes
to generate highly realistic synthetic faces exhibiting drug abuse traits
through augmentation. The proposed method, called "3DG-GA", Deep De-identified
anonymous Dataset Generation, uses Genetics Algorithm as a strategy for
synthetic faces generation. The algorithm includes GAN artificial face
generation, forgery detection, and face recognition. Initially, a dataset of
120 images of actual facial drug abuse is used. By preserving, the drug traits,
the 3DG-GA provides a dataset containing 3000 synthetic facial drug abuse
images. The dataset will be open to the scientific community, which can
reproduce our results and benefit from the generated datasets while avoiding
legal or ethical restrictions.
