Anthropometric analysis of facial dimensions using 3D imaging for forensic identification and ethnicity-specific reference models

Background

Facial morphology plays a crucial role in forensic identification, anthropological research, and reconstructive surgery. However, forensic reference databases often lack ethnicity-specific 3D anthropometric data, limiting the accuracy of forensic facial reconstruction and automated facial recognition systems. This study integrates 3D imaging technology and multivariate statistical analyses to enhance forensic facial identification models by providing ethnicity-specific facial measurements.

Methods

A cross-sectional study was conducted with 500 participants (250 males and 250 females) from seven Indian ethnic groups (Odia, Bengali, Tamil, Punjabi, Maratha, Telugu, and Gujarati). High-resolution 3D facial scans were obtained using the Artec Eva 3D scanner and analyzed using landmark-based anthropometry. Multivariate Analysis of Variance (MANOVA) assessed sex and ethnic differences in upper facial height (UFH), lower facial height (LFH), intercanthal distance (ICD), and face width (FW). Principal Component Analysis (PCA) and Structural Equation Modeling (SEM) were used to evaluate the interdependencies among facial dimensions.

Results

Males exhibited significantly larger UFH and ICD, while females had greater LFH (p < 0.001). Significant ethnic differences were observed (p < 0.01), with the Odia group having the widest face and the Bengali group showing the smallest ICD. PCA revealed two major components that explained 81.4 % of the total variance, with UFH and FW being the primary contributors. SEM demonstrated a strong correlation between UFH and FW (β = 0.72, p < 0.001) and an inverse relationship between LFH and ICD (β = −0.48, p = 0.002).

Conclusion

This study provides forensically relevant, ethnicity-specific 3D anthropometric data for facial reconstruction and forensic identification. These findings support the integration of 3D morphometric databases into forensic facial analysis software, enhancing population-specific identification accuracy. Future research should consider including Body Mass Index (BMI) as a variable to account for the potential impact of soft tissue distribution on facial morphology.