FCR: Investigating Generative AI models for Forensic Craniofacial Reconstruction
This addresses the time-consuming and expert-dependent nature of traditional forensic identification methods, though it appears incremental as it applies existing generative models to a new domain.
The authors tackled the problem of forensic craniofacial reconstruction from 2D X-ray images by developing a generative AI framework using models like CycleGANs and cGANs, achieving realistic face generation with evaluation metrics including FID, IS, and SSIM scores, and demonstrating effectiveness through a retrieval framework.
Craniofacial reconstruction in forensics is one of the processes to identify victims of crime and natural disasters. Identifying an individual from their remains plays a crucial role when all other identification methods fail. Traditional methods for this task, such as clay-based craniofacial reconstruction, require expert domain knowledge and are a time-consuming process. At the same time, other probabilistic generative models like the statistical shape model or the Basel face model fail to capture the skull and face cross-domain attributes. Looking at these limitations, we propose a generic framework for craniofacial reconstruction from 2D X-ray images. Here, we used various generative models (i.e., CycleGANs, cGANs, etc) and fine-tune the generator and discriminator parts to generate more realistic images in two distinct domains, which are the skull and face of an individual. This is the first time where 2D X-rays are being used as a representation of the skull by generative models for craniofacial reconstruction. We have evaluated the quality of generated faces using FID, IS, and SSIM scores. Finally, we have proposed a retrieval framework where the query is the generated face image and the gallery is the database of real faces. By experimental results, we have found that this can be an effective tool for forensic science.