3D Facial Anthropometry: Revolutionizing Forensic Identification with Ethnicity-Specific Models
Organized by: International Forensic Scientist Awards
Website: forensicscientist.org
15th Edition of Forensic Scientist Awards 27-28 October 2025 | Paris, France
Introduction
In the evolving field of forensic science, facial morphology serves as a key factor in identifying individuals and understanding population diversity. Traditional 2D anthropometric methods often lack the precision required for modern forensic applications. To address this gap, a recent study utilized 3D imaging technology to analyze facial dimensions across different Indian ethnic groups, creating ethnicity-specific reference models for forensic identification.
Background
Facial dimensions vary significantly among populations, influenced by genetics, environment, and lifestyle. However, most existing forensic reference databases fail to represent this ethnic diversity, which limits their effectiveness in facial reconstruction and recognition systems. The present study bridges this gap by applying 3D anthropometric analysis to establish a comprehensive facial reference dataset tailored to Indian ethnic diversity.
Methods
A cross-sectional study involving 500 participants (250 males and 250 females) from seven major Indian ethnic groups—Odia, Bengali, Tamil, Punjabi, Maratha, Telugu, and Gujarati—was conducted. Using the Artec Eva 3D scanner, researchers captured high-resolution facial scans. Landmark-based anthropometric measurements, including upper facial height (UFH), lower facial height (LFH), intercanthal distance (ICD), and face width (FW), were analyzed through MANOVA, Principal Component Analysis (PCA), and Structural Equation Modeling (SEM).
Results
The analysis revealed notable sex- and ethnicity-based differences in facial dimensions:
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Males showed significantly larger UFH and ICD, while females had greater LFH (p < 0.001).
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Significant ethnic differences were observed (p < 0.01): the Odia group exhibited the widest faces, whereas the Bengali group had the smallest ICD.
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PCA identified two major components accounting for 81.4 % of total variance, primarily influenced by UFH and FW.
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SEM indicated a strong positive correlation between UFH and FW (β = 0.72, p < 0.001) and a negative relationship between LFH and ICD (β = −0.48, p = 0.002).
Conclusion
This research highlights the importance of developing ethnicity-specific 3D facial databases to improve the accuracy of forensic facial reconstruction and automated identification systems. By integrating 3D morphometric data into forensic analysis software, investigators can achieve more reliable, population-specific identification outcomes.
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