Digital Forensic Facial Reconstruction using Population Specific Soft Tissue Thickness Data: A Comparative Study of...

Area of Science:

• Forensic Anthropology
• Digital Imaging
• Anatomical Modeling

Background:

• Accurate forensic facial reconstruction is crucial for identifying individuals.
• Computerized methods rely on soft tissue depth (FSTT) data, but regional variations can impact accuracy.
• Evaluating the influence of diverse FSTT datasets is essential for improving reconstruction reliability in specific populations.

Purpose of the Study:

• To assess the impact of different regional FSTT datasets on the accuracy of computerized forensic facial reconstructions.
• To determine which regional FSTT dataset provides the best accuracy for a contemporary Indian subject.
• To emphasize the importance of population-specific data in forensic applications.

Main Methods:

• A 3D skull model was created from a CT scan of an Indian male subject.
• Facial reconstructions were sculpted using Geomagic Freeform Plus and Touch X hardware.
• FSTT values from Gujarati, North Indian, and South Indian datasets were applied to place soft tissue markers.
• Root Mean Square (RMS) error and deviation maps were used to quantify reconstruction accuracy.

Main Results:

• The South Indian FSTT dataset resulted in the lowest RMS error (3.351 mm), indicating higher accuracy.
• The North Indian (3.754 mm) and Gujarati (4.376 mm) datasets showed progressively higher errors.
• Qualitative assessment revealed fewer errors in orbital and cheek regions with population-matched models.

Conclusions:

• Population-specific FSTT data significantly enhance the anatomical accuracy of forensic facial reconstructions.
• Utilizing comprehensive regional FSTT databases and advanced digital modeling is recommended for reliable human identification.
• Ongoing research is necessary to address data gaps and anatomical variability in forensic science.