A method for automatic forensic facial reconstruction based on dense statistics of soft tissue thickness

Area of Science:

• Forensic Anthropology
• Computer Vision
• Medical Imaging

Background:

• Accurate facial reconstruction from skeletal remains is crucial in forensic investigations.
• Existing methods often require manual intervention and specialized expertise.
• Statistical modeling offers a promising avenue for automating this process.

Purpose of the Study:

• To develop an automated method for human face estimation from skull remains.
• To create a robust statistical model for facial soft tissue thickness (FSTT).
• To facilitate forensic recovery of faces from incomplete skulls.

Main Methods:

• Utilized three statistical models: volumetric skull, surface head, and dense FSTT.
• Data acquired from computed tomography (CT) head scans and optical face scans.
• Registered skull models to CT scans to determine FSTT values for probabilistic face estimation.

Main Results:

• Developed a dense FSTT statistic in agreement with existing literature.
• Successfully generated plausible facial reconstructions from skull data.
• Validated the automated method against a real-world case with visual comparison.

Conclusions:

• The proposed automated method enables probabilistic face estimation for forensic applications.
• The FSTT statistic allows for the generation of adjustable and plausible head variants.
• This technique enhances the capabilities of forensic facial recovery, particularly from incomplete remains.