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

OBJECTIVES: 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. 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. RESULTS: 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. CONCLUSIONS: 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. CLINICAL SIGNIFICANCE: 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.