Superimposition-guided Facial Reconstruction from Skull

TL;DR

This paper introduces a novel facial reconstruction method from skulls using a database of portrait photos, superimposition, and generative models to improve forensic identification accuracy.

Contribution

It presents a new pipeline combining superimposition, autoencoder, and generative inpainting for more accurate skull-to-face reconstruction.

Findings

The pipeline is stable and accurate.

It outperforms traditional direct reconstruction methods.

Effective for forensic applications.

Abstract

We develop a new algorithm to perform facial reconstruction from a given skull. This technique has forensic application in helping the identification of skeletal remains when other information is unavailable. Unlike most existing strategies that directly reconstruct the face from the skull, we utilize a database of portrait photos to create many face candidates, then perform a superimposition to get a well matched face, and then revise it according to the superimposition. To support this pipeline, we build an effective autoencoder for image-based facial reconstruction, and a generative model for constrained face inpainting. Our experiments have demonstrated that the proposed pipeline is stable and accurate.