A Generative Deep Learning Approach for Forensic Facial Reconstruction

Abstract

Forensic facial reconstruction currently relies on subjective manual methods to reconstruct a recognizable face from a skull. Automated approaches using algorithms and statistical norms have been able to reliably construct faces in real-time, but are unable to generate areas of the face that do not correlate strongly to the bone beneath, such as the eyes, lips and ears. Recent developments in deep learning have shown that generative models can produce realistic images indistinguishable from genuine human faces. Applying these techniques, we propose a generative deep learning solution to perform facial reconstruction directly from the bone with limited data and no background expertise. The mo..