Development and validation of an automated algorithm for palatal rugae matching in forensic identification

Traditional forensic identification relies on DNA, fingerprints, and dental records, which may be unavailable in degraded remains. This study investigates palatal rugae as an alternative intraoral marker and evaluates a fully automated digital matching process with the hypothesis that it can achieve superior accuracy compared with semi-automated methods.

Palatal scans from 345 participants (11.2-73.2 years) were recorded using an intraoral scanner. After segmentation and Poisson disk downsampling (10,000 points), a four-step matching process was applied: (1) data preparation, (2) rough alignment via Fast Point Feature Histograms (FPFH), (3) fine alignment using the Iterative Closest Point (ICP) algorithm, and (4) distance-based similarity scoring. The algorithm was validated on 224 development scans and 111 unseen scans. Matching accuracy and sampling effects were analyzed.

The automated system achieved 100% accuracy in identifying identical scans and rejecting non-matching pairs. Random rotation confirmed robustness to initial alignment. Downsampling analysis showed that 3000 points suffice for perfect accuracy, reducing computational demand without loss of precision.

Automated 3D alignment of palatal rugae enables highly accurate, reproducible, and objective identification. The method outperforms semi-automated approaches and could significantly accelerate forensic workflows in mass casualty events. Further validation with post-mortem and scanner-diverse datasets is required, but results support integrating digital palatal data into standardized forensic identification protocols.

Automated 3D analysis of palatal rugae enables fast, reproducible and operator-independent identification from digital intraoral scans, supporting forensic workflows when conventional identifiers are unavailable.