Superimposition-guided Facial Reconstruction from Skull
This addresses the problem of identifying skeletal remains in forensic science, but it is incremental as it builds on existing reconstruction strategies with a novel pipeline.
The paper tackles facial reconstruction from skulls for forensic identification by using a database of portrait photos to generate face candidates, performing superimposition for matching, and revising the result, achieving stable and accurate performance.
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.